This document presents a study on using multimodal deep convolutional neural networks for non-destructive papaya fruit ripeness classification using digital and hyperspectral imaging systems. The study aims to develop a high-performance multimodal framework that utilizes both digital RGB images and hyperspectral data of papaya fruits. It reviews related work on fruit ripeness classification using various imaging modalities like hyperspectral and digital imaging. The study also discusses different approaches to multimodal deep learning like feature concatenation and late fusion that integrate information from multiple modalities.
ORGANIC PRODUCT DISEASE DETECTION USING CNNIRJET Journal
This document discusses using convolutional neural networks to detect diseases in organic products. The researchers classify fruits and their diseases using a mixed deep neural network and contour feature-based technique. They train their model on a dataset of 6509 rice leaf photos and test on 2000 images, achieving 99.6% accuracy. Their proposed system uses pre-trained ResNet50 models to extract deep features and classify plant diseases, offering advantages of lower cost, scalability, and time savings compared to manual inspection methods.
Plant Diseases Prediction Using Image ProcessingIRJET Journal
This document discusses a system for predicting plant diseases using image processing. Specifically, it focuses on predicting diseases that affect tomato plant leaves. The system uses convolutional neural network techniques to analyze images of tomato leaves and predict whether they have diseases like Late blight, bacterial, or viral infections. It discusses implementing various steps like pre-processing images, feature extraction, and using a CNN classifier to classify images as having a specific disease or being healthy. The goal is to help farmers quickly and accurately identify plant diseases from leaf images to improve crop management and reduce economic losses.
Convolutional Neural Network for Leaf and citrus fruit disease identification...IRJET Journal
This document presents a convolutional neural network model for identifying diseases in citrus fruits and leaves using deep learning. The researchers collected a dataset of 2,788 images across 7 different citrus diseases and trained two CNN architectures - AlexNet and LeNet - to classify the images. They achieved 98% accuracy in disease identification. The CNN models were able to learn discriminative features from the images to accurately predict the disease. The trained models were deployed through a Django web framework for easy use and prediction on new images. This model can help farmers quickly identify citrus diseases and take appropriate measures to control disease spread and improve crop yields.
IRJET- Identify Quality Index of the Fruit Vegetable by Non Destructive or wi...IRJET Journal
This document presents a literature review and proposed system for identifying the quality of fruits and vegetables using non-destructive image processing techniques. It discusses using computer vision algorithms like filtering, segmentation, feature extraction and classification to analyze images of fruits and determine quality metrics like size, shape, color and defects. The proposed system would capture images, preprocess them, extract features and classify fruits as good or defective quality without damaging the fruits. This could help automate quality inspection and grading of agricultural produce.
Using k means cluster and fuzzy c means for defect segmentation in fruitsIAEME Publication
This document summarizes a research paper that proposes using K-means clustering and Fuzzy C-means algorithms for defect segmentation in fruits. The paper begins with an introduction to quality assessment in food industries and computer vision techniques for inspection. It then discusses related works in fruit quality evaluation and defect detection. Next, it provides details of the K-means and Fuzzy C-means clustering algorithms. Finally, it describes implementing the clustering methods for defect segmentation on apple images, including preprocessing, feature extraction in color space, and classification.
Using k means cluster and fuzzy c means for defect segmentation in fruitsIAEME Publication
This document summarizes a paper that proposes using K-means clustering and Fuzzy C-means algorithms to segment defects in fruit images. The paper begins with an introduction on quality inspection in food industries and use of computer vision. It then discusses color spaces and image segmentation methods, focusing on clustering techniques. Related works on fruit quality inspection using techniques like filtering, segmentation and classification are also reviewed. The proposed approach segments defects from apple images based on color features using K-means and Fuzzy C-means clustering after preprocessing images with Gaussian filtering.
Using k means cluster and fuzzy c means for defect segmentation in fruitsIAEME Publication
This document summarizes a paper that proposes using K-means clustering and Fuzzy C-means algorithms to segment defects in fruit images. The paper begins with an introduction on quality inspection in food industries and use of computer vision. It then discusses image segmentation techniques, focusing on clustering algorithms. The paper presents applying K-means and Fuzzy C-means to segment defective regions of apple images based on color features. A comparison of the two methods is also performed.
ORGANIC PRODUCT DISEASE DETECTION USING CNNIRJET Journal
This document discusses using convolutional neural networks to detect diseases in organic products. The researchers classify fruits and their diseases using a mixed deep neural network and contour feature-based technique. They train their model on a dataset of 6509 rice leaf photos and test on 2000 images, achieving 99.6% accuracy. Their proposed system uses pre-trained ResNet50 models to extract deep features and classify plant diseases, offering advantages of lower cost, scalability, and time savings compared to manual inspection methods.
Plant Diseases Prediction Using Image ProcessingIRJET Journal
This document discusses a system for predicting plant diseases using image processing. Specifically, it focuses on predicting diseases that affect tomato plant leaves. The system uses convolutional neural network techniques to analyze images of tomato leaves and predict whether they have diseases like Late blight, bacterial, or viral infections. It discusses implementing various steps like pre-processing images, feature extraction, and using a CNN classifier to classify images as having a specific disease or being healthy. The goal is to help farmers quickly and accurately identify plant diseases from leaf images to improve crop management and reduce economic losses.
Convolutional Neural Network for Leaf and citrus fruit disease identification...IRJET Journal
This document presents a convolutional neural network model for identifying diseases in citrus fruits and leaves using deep learning. The researchers collected a dataset of 2,788 images across 7 different citrus diseases and trained two CNN architectures - AlexNet and LeNet - to classify the images. They achieved 98% accuracy in disease identification. The CNN models were able to learn discriminative features from the images to accurately predict the disease. The trained models were deployed through a Django web framework for easy use and prediction on new images. This model can help farmers quickly identify citrus diseases and take appropriate measures to control disease spread and improve crop yields.
IRJET- Identify Quality Index of the Fruit Vegetable by Non Destructive or wi...IRJET Journal
This document presents a literature review and proposed system for identifying the quality of fruits and vegetables using non-destructive image processing techniques. It discusses using computer vision algorithms like filtering, segmentation, feature extraction and classification to analyze images of fruits and determine quality metrics like size, shape, color and defects. The proposed system would capture images, preprocess them, extract features and classify fruits as good or defective quality without damaging the fruits. This could help automate quality inspection and grading of agricultural produce.
Using k means cluster and fuzzy c means for defect segmentation in fruitsIAEME Publication
This document summarizes a research paper that proposes using K-means clustering and Fuzzy C-means algorithms for defect segmentation in fruits. The paper begins with an introduction to quality assessment in food industries and computer vision techniques for inspection. It then discusses related works in fruit quality evaluation and defect detection. Next, it provides details of the K-means and Fuzzy C-means clustering algorithms. Finally, it describes implementing the clustering methods for defect segmentation on apple images, including preprocessing, feature extraction in color space, and classification.
Using k means cluster and fuzzy c means for defect segmentation in fruitsIAEME Publication
This document summarizes a paper that proposes using K-means clustering and Fuzzy C-means algorithms to segment defects in fruit images. The paper begins with an introduction on quality inspection in food industries and use of computer vision. It then discusses color spaces and image segmentation methods, focusing on clustering techniques. Related works on fruit quality inspection using techniques like filtering, segmentation and classification are also reviewed. The proposed approach segments defects from apple images based on color features using K-means and Fuzzy C-means clustering after preprocessing images with Gaussian filtering.
Using k means cluster and fuzzy c means for defect segmentation in fruitsIAEME Publication
This document summarizes a paper that proposes using K-means clustering and Fuzzy C-means algorithms to segment defects in fruit images. The paper begins with an introduction on quality inspection in food industries and use of computer vision. It then discusses image segmentation techniques, focusing on clustering algorithms. The paper presents applying K-means and Fuzzy C-means to segment defective regions of apple images based on color features. A comparison of the two methods is also performed.
This document proposes a method for stem removal of citrus fruit images using morphological image processing and thresholding. The method involves preprocessing images by resizing, converting to HSV color space, and removing noise using Gaussian filtering. Stem removal is then performed using morphological opening, distance transforms, top-hat filtering, and thresholding the grayscale values to isolate the stem pixels. The proposed stem removal process aims to accurately extract citrus fruit from images for classification.
LEAF DISEASE IDENTIFICATION AND REMEDY RECOMMENDATION SYSTEM USINGCNNIRJET Journal
This document presents a leaf disease identification and recommendation system using convolutional neural networks. The proposed system uses a dataset of 1500 plant images to train a CNN model to classify leaf diseases into 3 classes and identify recommended remedies. It involves data collection, preprocessing, training a CNN architecture with convolutional, activation and pooling layers to analyze images and detect diseases. The system is able to accurately identify different diseases of crops like cotton, sugarcane and wheat leaves. It provides a useful tool for farmers to detect diseases early and take appropriate treatment measures.
Leaf Disease Detection Using Image Processing and MLIRJET Journal
The document discusses using image processing and machine learning techniques like convolutional neural networks (CNNs) to detect plant leaf diseases. It proposes a system that uses CNNs to classify plant leaf images and detect diseases. The system would first preprocess leaf images, then extract features from them and feed them into a CNN model for classification. This could help farmers detect diseases early and improve crop productivity. The document reviews several related works applying CNNs and deep learning to tasks like mango leaf disease detection, tomato disease detection, and dragon fruit maturity detection with high accuracy. It outlines the proposed system architecture and algorithm and concludes CNNs can accurately detect plant diseases with reduced time and cost compared to manual methods.
Plant Monitoring using Image Processing, Raspberry PI & IOTIRJET Journal
This document describes a plant monitoring system using image processing, a Raspberry Pi, and the Internet of Things. The system uses image processing techniques like segmentation, feature extraction and classification on images of plant leaves to detect diseases. Sensors connected to an Arduino board such as a humidity sensor, gas sensors and a light sensor are used to monitor environmental conditions. The Arduino and Raspberry Pi are connected to allow the sensors data to be sent to the Raspberry Pi. The Raspberry Pi then sends notifications about the plant health and environmental conditions to smartphones. This allows remote monitoring of farm conditions.
Precision agriculture relies heavily on information technology, which also aids agronomists in their work.
Weeds usually grow alongside crops, reducing the production of that crop. Weeds are controlled by herbicides. The pesticide may harm the crop as well if the type of weed isn’t identified. In order to control weeds on farms, it is required to identify and classify them. Convolutional Network or CNN, a deep learning-based computer vision technology, is used to evaluate images. A methodology is proposed to detect weed using convolutional neural networks.
There were two primary phases in this proposed methodology. The first phase is image collection and labeling, in which the features for images to be labeled for the base images are extracted. In second phase to build the convolutional neural network model is constructed by 20 layers to detect the weed. CNN architecture has three layers
namely convolutional layer, pooling layer and dense layer. The input image is given to convolutional layer to extract the features from the image. The features are given to pooling layer to compress the image to reduce the computational complexity. The dense layer is used for final classification. The performance of the proposed methodology is assessed using agricultural dataset images taken from Kaggle database.
Plant disease detection system using image processingIRJET Journal
This document describes a study that used image processing and convolutional neural networks to develop a system for detecting plant diseases from images of plant leaves. The researchers created a model using the PlantVillage dataset of over 55,000 photos of leaves with 38 different disease labels. They augmented the data and used a CNN architecture with convolutional, pooling, ReLU, and fully connected layers to achieve 95.3% accuracy in classifying disease labels, outperforming conventional detection methods. The goal was to help farmers efficiently identify diseases early and apply appropriate treatments to prevent crop loss and economic impacts.
Techniques of deep learning and image processing in plant leaf disease detect...IJECEIAES
Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated.
This document describes a project to detect fruit diseases using artificial neural networks and image processing techniques. It proposes a system that uses OpenCV, k-means clustering, and a neural network to identify diseases by analyzing images of fruit. The system would have modules for creating and preprocessing datasets to train a model, as well as modules for users to register, upload images of fruit, and receive predictions of potential diseases. The goal is to build an accurate and low-cost solution to assist farmers in identifying diseases early and improving crop yields.
REAL FRUIT DEFECTIVE DETECTION BASED ON IMAGE PROCESSING TECHNIQUES USING OPENCVIRJET Journal
This document discusses a research project that aims to develop a computer vision system using OpenCV to detect defects in fruits based on image processing techniques. The system would analyze images of fruits to determine quality by examining features like color, texture, and size. Such a system could help automate quality inspection in the fruit industry in India in a way that is more efficient and objective than manual methods. It provides background on the importance of fruit production in India and the need for automation. The proposed approach involves preprocessing images, extracting features, and analyzing the images to classify fruits as defective or not defective. Benefits of this system include easier quality assessment and more consistent evaluations.
This document presents a technique for detecting defects on fruits using computer vision. It uses color images of fruits and performs feature extraction to extract color, edge, and texture features. A k-means clustering algorithm is then used to classify the fruits based on their extracted features. The k-means algorithm clusters the fruits into k groups based on the features. This technique allows for detection of defects on fruits in an automated way, providing a robust solution and improving quality control in the food and agricultural industries.
AUTOMATIC FRUIT RECOGNITION BASED ON DCNN FOR COMMERCIAL SOURCE TRACE SYSTEMijcsa
Automatically fruit recognition by using machine vision is considered as challenging task due to similarities between various types of fruits and external environmental changes e-g lighting. In this paper, fruit recognition algorithm based on Deep Convolution Neural Network(DCNN) is proposed. Most of the previous techniques have some limitations because they were examined and evaluated under limited dataset, furthermore they have not considered external environmental changes. Another major contribution in this paper is that we established fruit images database having 15 different categories comprising of 44406 images which were collected within a period of 6 months by keeping in view the limitations of existing dataset under different real-world conditions. Images were directly used as input to DCNN for training and recognition without extracting features, besides this DCNN learn optimal features from images through adaptation process. The final decision was totally based on a fusion of all regional classification using probability mechanism. Experimental results exhibit that the proposed approach have efficient capability of automatically recognizing the fruit with a high accuracy of 99% and it can also effectively meet real world application requirements.
AUTOMATIC FRUIT RECOGNITION BASED ON DCNN FOR COMMERCIAL SOURCE TRACE SYSTEMijcsa
Automatically fruit recognition by using machine vision is considered as challenging task due to similarities between various types of fruits and external environmental changes e-g lighting. In this paper, fruit recognition algorithm based on Deep Convolution Neural Network(DCNN) is proposed. Most of the
previous techniques have some limitations because they were examined and evaluated under limited dataset, furthermore they have not considered external environmental changes. Another major contribution in this paper is that we established fruit images database having 15 different categories comprising of
44406 images which were collected within a period of 6 months by keeping in view the limitations of existing dataset under different real-world conditions. Images were directly used as input to DCNN for training and recognition without extracting features, besides this DCNN learn optimal features from
images through adaptation process. The final decision was totally based on a fusion of all regional classification using probability mechanism. Experimental results exhibit that the proposed approach have efficient capability of automatically recognizing the fruit with a high accuracy of 99% and it can also
effectively meet real world application requirements.
Improved vision-based diagnosis of multi-plant disease using an ensemble of d...IJECEIAES
Farming and plants are crucial parts of the inward economy of a nation, which significantly boosts the economic growth of a country. Preserving plants from several disease infections at their early stage becomes cumbersome due to the absence of efficient diagnosis tools. Diverse difficulties lie in existing methods of plant disease recognition. As a result, developing a rapid and efficient multi-plant disease diagnosis system is a challenging task. At present, deep learning-based methods are frequently utilized for diagnosing plant diseases, which outperformed existing methods with higher efficiency. In order to investigate plant diseases more accurately, this article addresses an efficient hybrid approach using deep learning-based methods. Xception and ResNet50 models were applied for the classification of plant diseases, and these models were merged using the stacking ensemble learning technique to generate a hybrid model. A multi-plant dataset was created using leaf images of four plants: black gram, betel, Malabar spinach, and litchi, which contains nine classes and 44,972 images. Compared to existing individual convolutional neural networks (CNN) models, the proposed hybrid model is more feasible and effective, which acquired 99.20% accuracy. The outcomes and comparison with existing methods represent that the designed method can acquire competitive performance on the multi-plant disease diagnosis tasks.
Plant Leaf Disease Detection and Classification Using Image ProcessingIRJET Journal
The document summarizes a research paper on detecting and classifying plant leaf diseases using image processing techniques. It begins by discussing the importance of identifying plant diseases early. It then provides an overview of traditional identification methods and their limitations. Next, it describes how image processing can be used to extract features from leaf images and classify diseases using machine learning algorithms. The paper evaluates several studies that have achieved accuracy ranging from 80-99.8% using different approaches. It also discusses challenges like variable image quality and limited datasets, and potential solutions. Finally, it presents results showing accuracy of 95-99% for different techniques depending on the dataset and diseases studied.
FRUIT DISEASE DETECTION AND CLASSIFICATION USING ARTIFICIAL INTELLIGENCEIRJET Journal
This document proposes a method to detect and classify diseases in fruits like banana, apple, and orange using artificial intelligence techniques. The method uses convolutional neural networks and k-means clustering. Fruit images are preprocessed, features like color, shape, and size are extracted, and k-means clustering is used to categorize the images into clusters. A convolutional neural network is then used to classify whether each fruit in the image is infected or not infected. The method achieved 95% accuracy in identifying diseases in banana, apple, and orange fruits.
Android application for detection of leaf disease (Using Image processing and...IRJET Journal
This document describes an Android application for detecting leaf diseases using image processing and neural networks. The application uses a convolutional neural network (CNN) model trained on a dataset of images of healthy and unhealthy plant leaves. The CNN classifies leaf images uploaded by users to identify the disease and provide an accurate diagnosis. The application aims to help farmers and students quickly identify plant diseases to control their spread and reduce agricultural losses. It analyzes leaf images using techniques like preprocessing, augmentation, feature extraction, and classification with a CNN architecture. The trained model is integrated into an Android application using TensorFlow Lite to enable real-time disease detection from smartphone photos of leaves.
AN IMAGE PROCESSING APPROACHES ON FRUIT DEFECT DETECTION USING OPENCVIRJET Journal
This document discusses an image processing approach for detecting defects in fruits using OpenCV. It begins with an introduction to the importance of fruit production in India and the need for automation in the agriculture industry. It then discusses how previous manual quality inspection methods are inefficient. The proposed system uses computer vision and image processing techniques like OpenCV to analyze fruit images and detect defects by examining characteristics like color, texture and size. It describes the image preprocessing, thresholding, noise removal and edge detection steps. The system is able to successfully identify normal and defective fruits in images to classify quality. This provides an improved quality inspection method that is objective, consistent and can be applied to various agricultural products.
This document summarizes a research paper that proposes using convolutional neural networks to detect plant diseases through images of plant leaves. It presents three CNN architectures - Faster R-CNN, R-FCN, and SSD - that were examined for the task. The paper describes preprocessing the Plant Village dataset, training models using data augmentation, and achieving 94.6% accuracy in validating the models. It concludes that CNNs show feasibility for an AI-based solution to complex plant disease detection problems.
IRJET-Android Based Plant Disease Identification System using Feature Extract...IRJET Journal
This document summarizes a research paper that proposes an Android-based plant disease identification system using image processing techniques. The system uses feature extraction and pattern matching to analyze images of infected plant leaves and identify the disease. It segments images using k-means clustering and extracts features like color, morphology, texture and lesions. These features are matched against a trained database of infected images using SURF pattern matching to diagnose the disease. The system is intended to help farmers and experts identify diseases early at a lower cost than other methods. It aims to achieve accurate identification of diseases for grapes, apples and pomegranates through this automated mobile-based approach.
Foliage Measurement Using Image Processing TechniquesIJTET Journal
Automatic detection of fruit and leaf diseases is essential to automatically detect the symptoms of diseases as early as they appear on the growing stage. This system helps to detect the diseases on fruit during farming , right from plan and easily monitoring the diseases of grapes leaf and apple fruit. By using this system we can avoid the economical loss due to various diseases in agriculture production. K-means clustering technique is used for segmentation. The features are extracted from the segmented image and artificial neural network is used for training the image database and classified their performance to the respective disease categories. The experimental results express that what type of disease can be affected in the fruit and leaf .
This document proposes a method for stem removal of citrus fruit images using morphological image processing and thresholding. The method involves preprocessing images by resizing, converting to HSV color space, and removing noise using Gaussian filtering. Stem removal is then performed using morphological opening, distance transforms, top-hat filtering, and thresholding the grayscale values to isolate the stem pixels. The proposed stem removal process aims to accurately extract citrus fruit from images for classification.
LEAF DISEASE IDENTIFICATION AND REMEDY RECOMMENDATION SYSTEM USINGCNNIRJET Journal
This document presents a leaf disease identification and recommendation system using convolutional neural networks. The proposed system uses a dataset of 1500 plant images to train a CNN model to classify leaf diseases into 3 classes and identify recommended remedies. It involves data collection, preprocessing, training a CNN architecture with convolutional, activation and pooling layers to analyze images and detect diseases. The system is able to accurately identify different diseases of crops like cotton, sugarcane and wheat leaves. It provides a useful tool for farmers to detect diseases early and take appropriate treatment measures.
Leaf Disease Detection Using Image Processing and MLIRJET Journal
The document discusses using image processing and machine learning techniques like convolutional neural networks (CNNs) to detect plant leaf diseases. It proposes a system that uses CNNs to classify plant leaf images and detect diseases. The system would first preprocess leaf images, then extract features from them and feed them into a CNN model for classification. This could help farmers detect diseases early and improve crop productivity. The document reviews several related works applying CNNs and deep learning to tasks like mango leaf disease detection, tomato disease detection, and dragon fruit maturity detection with high accuracy. It outlines the proposed system architecture and algorithm and concludes CNNs can accurately detect plant diseases with reduced time and cost compared to manual methods.
Plant Monitoring using Image Processing, Raspberry PI & IOTIRJET Journal
This document describes a plant monitoring system using image processing, a Raspberry Pi, and the Internet of Things. The system uses image processing techniques like segmentation, feature extraction and classification on images of plant leaves to detect diseases. Sensors connected to an Arduino board such as a humidity sensor, gas sensors and a light sensor are used to monitor environmental conditions. The Arduino and Raspberry Pi are connected to allow the sensors data to be sent to the Raspberry Pi. The Raspberry Pi then sends notifications about the plant health and environmental conditions to smartphones. This allows remote monitoring of farm conditions.
Precision agriculture relies heavily on information technology, which also aids agronomists in their work.
Weeds usually grow alongside crops, reducing the production of that crop. Weeds are controlled by herbicides. The pesticide may harm the crop as well if the type of weed isn’t identified. In order to control weeds on farms, it is required to identify and classify them. Convolutional Network or CNN, a deep learning-based computer vision technology, is used to evaluate images. A methodology is proposed to detect weed using convolutional neural networks.
There were two primary phases in this proposed methodology. The first phase is image collection and labeling, in which the features for images to be labeled for the base images are extracted. In second phase to build the convolutional neural network model is constructed by 20 layers to detect the weed. CNN architecture has three layers
namely convolutional layer, pooling layer and dense layer. The input image is given to convolutional layer to extract the features from the image. The features are given to pooling layer to compress the image to reduce the computational complexity. The dense layer is used for final classification. The performance of the proposed methodology is assessed using agricultural dataset images taken from Kaggle database.
Plant disease detection system using image processingIRJET Journal
This document describes a study that used image processing and convolutional neural networks to develop a system for detecting plant diseases from images of plant leaves. The researchers created a model using the PlantVillage dataset of over 55,000 photos of leaves with 38 different disease labels. They augmented the data and used a CNN architecture with convolutional, pooling, ReLU, and fully connected layers to achieve 95.3% accuracy in classifying disease labels, outperforming conventional detection methods. The goal was to help farmers efficiently identify diseases early and apply appropriate treatments to prevent crop loss and economic impacts.
Techniques of deep learning and image processing in plant leaf disease detect...IJECEIAES
Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated.
This document describes a project to detect fruit diseases using artificial neural networks and image processing techniques. It proposes a system that uses OpenCV, k-means clustering, and a neural network to identify diseases by analyzing images of fruit. The system would have modules for creating and preprocessing datasets to train a model, as well as modules for users to register, upload images of fruit, and receive predictions of potential diseases. The goal is to build an accurate and low-cost solution to assist farmers in identifying diseases early and improving crop yields.
REAL FRUIT DEFECTIVE DETECTION BASED ON IMAGE PROCESSING TECHNIQUES USING OPENCVIRJET Journal
This document discusses a research project that aims to develop a computer vision system using OpenCV to detect defects in fruits based on image processing techniques. The system would analyze images of fruits to determine quality by examining features like color, texture, and size. Such a system could help automate quality inspection in the fruit industry in India in a way that is more efficient and objective than manual methods. It provides background on the importance of fruit production in India and the need for automation. The proposed approach involves preprocessing images, extracting features, and analyzing the images to classify fruits as defective or not defective. Benefits of this system include easier quality assessment and more consistent evaluations.
This document presents a technique for detecting defects on fruits using computer vision. It uses color images of fruits and performs feature extraction to extract color, edge, and texture features. A k-means clustering algorithm is then used to classify the fruits based on their extracted features. The k-means algorithm clusters the fruits into k groups based on the features. This technique allows for detection of defects on fruits in an automated way, providing a robust solution and improving quality control in the food and agricultural industries.
AUTOMATIC FRUIT RECOGNITION BASED ON DCNN FOR COMMERCIAL SOURCE TRACE SYSTEMijcsa
Automatically fruit recognition by using machine vision is considered as challenging task due to similarities between various types of fruits and external environmental changes e-g lighting. In this paper, fruit recognition algorithm based on Deep Convolution Neural Network(DCNN) is proposed. Most of the previous techniques have some limitations because they were examined and evaluated under limited dataset, furthermore they have not considered external environmental changes. Another major contribution in this paper is that we established fruit images database having 15 different categories comprising of 44406 images which were collected within a period of 6 months by keeping in view the limitations of existing dataset under different real-world conditions. Images were directly used as input to DCNN for training and recognition without extracting features, besides this DCNN learn optimal features from images through adaptation process. The final decision was totally based on a fusion of all regional classification using probability mechanism. Experimental results exhibit that the proposed approach have efficient capability of automatically recognizing the fruit with a high accuracy of 99% and it can also effectively meet real world application requirements.
AUTOMATIC FRUIT RECOGNITION BASED ON DCNN FOR COMMERCIAL SOURCE TRACE SYSTEMijcsa
Automatically fruit recognition by using machine vision is considered as challenging task due to similarities between various types of fruits and external environmental changes e-g lighting. In this paper, fruit recognition algorithm based on Deep Convolution Neural Network(DCNN) is proposed. Most of the
previous techniques have some limitations because they were examined and evaluated under limited dataset, furthermore they have not considered external environmental changes. Another major contribution in this paper is that we established fruit images database having 15 different categories comprising of
44406 images which were collected within a period of 6 months by keeping in view the limitations of existing dataset under different real-world conditions. Images were directly used as input to DCNN for training and recognition without extracting features, besides this DCNN learn optimal features from
images through adaptation process. The final decision was totally based on a fusion of all regional classification using probability mechanism. Experimental results exhibit that the proposed approach have efficient capability of automatically recognizing the fruit with a high accuracy of 99% and it can also
effectively meet real world application requirements.
Improved vision-based diagnosis of multi-plant disease using an ensemble of d...IJECEIAES
Farming and plants are crucial parts of the inward economy of a nation, which significantly boosts the economic growth of a country. Preserving plants from several disease infections at their early stage becomes cumbersome due to the absence of efficient diagnosis tools. Diverse difficulties lie in existing methods of plant disease recognition. As a result, developing a rapid and efficient multi-plant disease diagnosis system is a challenging task. At present, deep learning-based methods are frequently utilized for diagnosing plant diseases, which outperformed existing methods with higher efficiency. In order to investigate plant diseases more accurately, this article addresses an efficient hybrid approach using deep learning-based methods. Xception and ResNet50 models were applied for the classification of plant diseases, and these models were merged using the stacking ensemble learning technique to generate a hybrid model. A multi-plant dataset was created using leaf images of four plants: black gram, betel, Malabar spinach, and litchi, which contains nine classes and 44,972 images. Compared to existing individual convolutional neural networks (CNN) models, the proposed hybrid model is more feasible and effective, which acquired 99.20% accuracy. The outcomes and comparison with existing methods represent that the designed method can acquire competitive performance on the multi-plant disease diagnosis tasks.
Plant Leaf Disease Detection and Classification Using Image ProcessingIRJET Journal
The document summarizes a research paper on detecting and classifying plant leaf diseases using image processing techniques. It begins by discussing the importance of identifying plant diseases early. It then provides an overview of traditional identification methods and their limitations. Next, it describes how image processing can be used to extract features from leaf images and classify diseases using machine learning algorithms. The paper evaluates several studies that have achieved accuracy ranging from 80-99.8% using different approaches. It also discusses challenges like variable image quality and limited datasets, and potential solutions. Finally, it presents results showing accuracy of 95-99% for different techniques depending on the dataset and diseases studied.
FRUIT DISEASE DETECTION AND CLASSIFICATION USING ARTIFICIAL INTELLIGENCEIRJET Journal
This document proposes a method to detect and classify diseases in fruits like banana, apple, and orange using artificial intelligence techniques. The method uses convolutional neural networks and k-means clustering. Fruit images are preprocessed, features like color, shape, and size are extracted, and k-means clustering is used to categorize the images into clusters. A convolutional neural network is then used to classify whether each fruit in the image is infected or not infected. The method achieved 95% accuracy in identifying diseases in banana, apple, and orange fruits.
Android application for detection of leaf disease (Using Image processing and...IRJET Journal
This document describes an Android application for detecting leaf diseases using image processing and neural networks. The application uses a convolutional neural network (CNN) model trained on a dataset of images of healthy and unhealthy plant leaves. The CNN classifies leaf images uploaded by users to identify the disease and provide an accurate diagnosis. The application aims to help farmers and students quickly identify plant diseases to control their spread and reduce agricultural losses. It analyzes leaf images using techniques like preprocessing, augmentation, feature extraction, and classification with a CNN architecture. The trained model is integrated into an Android application using TensorFlow Lite to enable real-time disease detection from smartphone photos of leaves.
AN IMAGE PROCESSING APPROACHES ON FRUIT DEFECT DETECTION USING OPENCVIRJET Journal
This document discusses an image processing approach for detecting defects in fruits using OpenCV. It begins with an introduction to the importance of fruit production in India and the need for automation in the agriculture industry. It then discusses how previous manual quality inspection methods are inefficient. The proposed system uses computer vision and image processing techniques like OpenCV to analyze fruit images and detect defects by examining characteristics like color, texture and size. It describes the image preprocessing, thresholding, noise removal and edge detection steps. The system is able to successfully identify normal and defective fruits in images to classify quality. This provides an improved quality inspection method that is objective, consistent and can be applied to various agricultural products.
This document summarizes a research paper that proposes using convolutional neural networks to detect plant diseases through images of plant leaves. It presents three CNN architectures - Faster R-CNN, R-FCN, and SSD - that were examined for the task. The paper describes preprocessing the Plant Village dataset, training models using data augmentation, and achieving 94.6% accuracy in validating the models. It concludes that CNNs show feasibility for an AI-based solution to complex plant disease detection problems.
IRJET-Android Based Plant Disease Identification System using Feature Extract...IRJET Journal
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口試簡報.pptx
1. Multimodal Deep Convolutional Neural Networks for Non-destructive
Papaya Fruit Ripeness Classification Using Digital
and Hyperspectral Imaging Systems
Cinmayii A. Garillos-Manliguez (秦瑪怡)
Ph.D. Candidate
Prof. John Y. Chiang (蔣依吾 教授)
Advisor, Image Processing Laboratory
Department of Computer Science and Engineering
2. Outline
• Introduction
• Review of Literature
• Imaging Systems
• Hyperspectral Image
Classification
• Deep Learning
• Multimodality
• Fruit Ripeness Classification
• Materials and Methods
• Data Gathering
• Digital Image Acquisition
• Hyperspectral DataAcquisition
• Computing Environment
• Performance Evaluation Metrics
• Papaya Fruit Ripeness
Classification
• Multimodal Deep Learning
Framework
• Multimodality via Feature
Concatenation
• Multimodality via Late Fusion
• Multimodality via End-to-end
Deep Convolutional Neural
Network
• Benchmarking of Results
• Conclusion
4. Introduction
Artificial intelligence, computer vision, and its
applications involve highly interdisciplinary research.
The impact of these areas in computer science
continues to influence the technological advances in
our society today.
Image classification is a challenging task yet it plays
a valuable role in computer vision since it is the basis
of higher-order operations
5. Introduction
ImageClassification
Image classification has gained much attention in
various applications including biomedical, medical
and agricultural applications.
It performs a critical function in cancer detection and
disease diagnosis because the results are life-
changing.
The classification result is also significant in the
agricultural sector because it determines the market
distribution of the produce which affects the
potential gain or loss in the local or national revenue.
6. Introduction
Images used in these applications come from
sensing or imaging devices that acquire the
electromagnetic energy emitted by the target
object.
PET images of bone scans from gamma ray imaging
X-ray images from X-ray tomography
UV light images used in fluorescence microscopy
Spaceborne radar images from microwave bands
Magnetic resonance imaging (MRI) images in medicine
Images convey information thru computer vision:
size, shape, patterns, and other salient features from
RGB or hyperspectral images
Fig. 1.1. The electromagnetic spectrum and the visible light range (Lifted from [2]).
7. Introduction
Image classification techniques are successful in
unimodal or single modality implementations.
Modality in human-computer interaction is defined
as the categorization of a distinct individual channel
of sensory input or output between a computer and
a human.
Multimodal learning, a recent development inAI,
involves multiple data types or input modalities to
perform a given task.
8. Introduction
Fruit RipenessClassification
Fruits have vital role in human health since these are
abundant in antioxidants, nutrients, vitamins,
minerals, and fiber that the human body needs.
Consumers prefer high quality, blemish-free, and
safe fresh fruits.
Papaya, Carica papaya L., a major tropical fruit
worldwide, gives many health and economic
benefits.
9. Introduction
Fruit ripeness/maturity – physiological development as
the fruit ripens even after harvest.
Under-mature fruit – reduced taste quality
Over-mature fruit – overripening during transport,
prone to mechanical damages, increase susceptibility
to decay and microbial growth
Critical factor to preserve post-harvest life, reduce
waste, and increase fruit quality
Food losses - more than 40% occur at postharvest and
processing levels in developing countries, while more
than 40% occur at retail and consumer levels in
industrialized countries
10. Introduction
Efficient Fruit MaturityAssessment andGrading
Well-trained personnel with experience – human error
Traditional laboratory processes – destructive and time-
consuming
Non-invasive methods through computer vision (CV)
techniques, artificial intelligence (AI), sensors and imaging
technologies provided promising results while keeping
the fruit in good shape, which help reduce post-harvest
losses
Hyperspectral imaging has become an emerging
scientific instrument for non-destructive fruit and
vegetable quality assessment in recent years]
11. Main Objective
This dissertation primarily aims to propose a high-
performance multimodal framework of deep
convolutional neural network for non-destructive
papaya fruit ripeness classification that make use of
digital and hyperspectral imaging technology.
12. Key Contributions
(1) An original dataset of hyperspectral data cubes and digital (RGB) photos of papaya fruits, which
are categorized into six ripeness stages, acquired within a laboratory environment;
(2) An analysis of multimodality in deep learning through implementing feature concatenation
approach and late fusion of imaging-specific deep CNNs to an agricultural application, particularly
to non-destructive papaya fruit ripeness classification assessed in terms of F1-score, accuracy, top-
2 error rate, precision, recall, training time, and average prediction time; and
(3) A multimodal deep CNN architecture producing outstanding performance by discovering
abstract representations of two modalities, which are an RGB image and a hyperspectral
reflectance data cube of papaya fruit.
13. Summary and Organization
The remaining parts of this paper is organized as:
Section (2) presents the studies related to this paper
Section (3) provides a description of a multimodal deep
learning architecture with feature concatenation and
late fusion implementations for papaya fruit
classification using hyperspectral reflectance data set
and RGB images;
Section (4) discusses the experiments, results of the
experimental work and benchmarking; and
Section (5) concludes this work and suggests future
research directions.
15. Review of Literature
• Machine learning is a primary contributor in the
advancement of AI
• Deep learning is very streamlined nowadays despite
the challenges in cost and infrastructure.
• For instance, its applications in agriculture include
detection and classification of fruits, vegetables,
crops, and weeds in the field or inside the laboratory
or factories [5], [17], [18], estimation of fruit maturity
or ripeness using transfer learning [19], and detection
of bruise or damages on fruits [20].
16. Review of Literature
Imaging Systems
Imaging technologies like hyperspectral imaging (HSI)
system and visible-light imaging have been widely
investigated as non-destructive options for food
production and agricultural applications [11][12] for:
• fruit maturity or ripeness estimation [11],
• automatic fruit grading [9], [36],
• disease detection [26], [27],
• disease classification [26], [28], and
• agro-food product inspection [21].
17. Review of Literature
Imaging Systems
Table 2.1. Summary of grading
characteristics that can be identified
and assessed using visible and
hyperspectral imaging based on
these review papers [15], [21], [22].
Hyperspectral Imaging Visible Imaging
Soluble solid content
Firmness
Dry matter
Water content
Total soluble solids
Titratable acidity
Defect (Bruise)
Decay (Sour skin, canker)
Contamination
Insect damage
Mealiness
Color
Maturity (pre-harvest time)
Anthocyanins
Total chlorophyll
Lycopene
Total phenols
Total glucosilonates
Polyphenol oxidase activity
Ascorbic acid
Carotenoid
Grading by color (color evaluation)
Grading by external quality
Sorting by external quality
Color and size classification
Shape grading
Irregularity evaluation and sorting
Length
Width
Thickness
Texture
Firmness
18. Review of Literature
Hyperspectral Image Classification (HIC)
• Hyperspectral imaging is a powerful tool in
acquisition of highly dimensional data with a
high level of precision
• HIC uses hyperspectral band segments (H) in
m × n × h three-dimensional images, where
m is the length and n is the width of the
spatial dimension and h is the number of
bands
• Remote sensing applications. Each band is
composed of points in a 3D matrix (x, y, and z
positions), its coordinates in longitude and
latitude and the intensity value which relates
to its radiance.
19. Review of Literature
Single-image classification
• Contrary to remote sensing where one
image is composed of many labels, a
hypercube containing the entire object to be
classified may only contain one label or class.
• This is the case in this study: each HS data
cube is categorized into one maturity stage,
which is a very challenging task because of
its high dimensionality and requires a large
memory capacity and higher computing
resources.
20. Review of Literature
Single-image classification
• Damage detection on blueberries [20]:
• spectral data ranging from 328.81 nm up to
1113.54 nm
• reduce the spatial channels from 1002 to
151 by subsampling, and then, reducing the
image size to 32x32 pixels to reduce
computational cost
• Fruit classification [39]
• Fruit ripeness such as persimmon [40],
strawberry [41], [42], blueberry [43],
cherry [44], kiwi [45], banana [46], and
mango [47], [48].
21. Review of Literature
RelatedWorks on Single-imageClassification for Fruit Ripeness
Fruit Ripeness
Classification
Wavelength range (nm) Methodology Classification
Result
Persimmon [40] 400 – 1000 Linear Discriminant Analysis (LDA)
on three feature wavelengths (4 ripeness
stages)
95.3%
Strawberry[41],[42] 380 – 1030 and 874 – 1734 [41]
370 – 1015 [42]
SVMs on wavelengths selected by PCA
(3 ripeness stages) [41]
AlexNet on wavelengths chosen by PCA
(2 ripeness stages) [42]
85%
98.6%
Cherry [44] 874 – 1734 Genetic algorithm (GA) and multiple linear
regression (MLR)
96.4%
Mango [47], [48] 411.3 - 867.0 [48] Unmanned Ground Vehicle (UGV) with GA
and SVM
PLS-DA and CNN [48]
0.69
0.97
Papaya (This study) 470 – 900 Multimodal Deep Convolutional Neural
Networks
22. Review of Literature
Deep Learning
• Deep learning is the prime mover that has
brought breakthroughs in processing single
modalities whether these are sequential like
text or speech/audio or discrete such as
images and videos.
• The convolutional layers in a deep
convolutional neural network (deep CNN)
enable it to closely find the patterns that
identify an image (requires minimal human
engineering)
23. Deep learning
AlexNet
• Consist of eight layers, which is faster to train and ideal for real-world
applications
• ReLU nonlinearity, overlapping pooling, dropouts
VGG16 and VGG19
• State-of-the-art methods in large-scale image recognition tasks
• Very small 3×3 convolution filters
• Increased depth, more representations, improved network accuracy
ResNet
• Implements residual learning to resolve degradation problem
• Highly modularized networks
• Stack of blocks (layer group)
ResNeXt
• Homogenous and multi-branch architecture
• Assembly of redundant building blocks with the same configuration
• Introduced a new dimension: cardinality
MobileNet
• Mobile and embedded applications
• Depth-wise separable convolutions (DSC)
MobileNetV2
• Memory-efficient implementation
• Inverted residual structure
• Thin bottleneck layers has shortcuts and lightweight DSC is used
Key features of the
deep convolutional
neural networks
used in this study
24. Review of Literature
Multimodality
• Deep learning in multimodality is
characterized by its ability to automatically
extract specialized features from an input
instance.
• Feature concatenation (FC) and ensemble
method (EM) are two general approaches to
implement multimodal learning schemes.
25. Review of Literature
Multimodality
• Feature concatenation (FC): A single
feature vector is generated by
integrating data from several different
modalities at an early phase
• Ensemble method (EM): a late-fusion
method that does train and learn the
characteristics of each modality before
integrating the results.
26. Review of Literature
Multimodality
• Multimodality overcomes the disadvantage of RGB
images in providing 3D indicators by integrating infrared
and depth images to obtain 2D local features and 3D
sparse point cloud for geometric verification (F1-score
of 0.77) [3].
• Multimodality is used for detection and classification of
fruits in real-time [5].The system employs two
mechanisms, early fusion (0.799) and late fusion (0.838),
to use RGB and NIR images.
• In this work, the author implemented both feature
concatenation and late fusion learning strategies on
deep CNNs to enable multimodality.
27. Review of Literature
Multimodality
• Multimodality overcomes the disadvantage of RGB
images in providing 3D indicators by integrating infrared
and depth images to obtain 2D local features and 3D
sparse point cloud for geometric verification (F1-score
of 0.77) [3].
• Multimodality is used for detection and classification of
fruits in real-time [5].The system employs two
mechanisms, early fusion (0.799) and late fusion (0.838),
to use RGB and NIR images.
• In this work, the author implemented both feature
concatenation and late fusion learning strategies on
deep CNNs to enable multimodality.
29. Methodology
• Papaya fruit (Carica papaya L.)
samples, a total of 253 pieces, were
obtained from Kaohsiung Market in
Gushan District,Taiwan and were
ripened in a controlled environment.
• Data collection every Day 1, Day 3, Day
5 and Day 7 to ensure ripening
according to [59].
• Digital RGB images from at least four
sides: front, back, left, and right, and
two orientations of each side:
horizontal and diagonal orientation.
30. Methodology
• The Philippine National Standard
(PNS/BAFPS 33:2005) is the basis of
this classification standard for the
ground truth data [59].
• Sample images of the papaya fruits
classified based on the said maturity
stages are shown in Figure 3.2.
• Each description of these stages are
summarized inTable 3.1.
31. Digital ImageAcquisition
The camera specifications used in this
setting are:
• Canon EOS 100D
• 18.5 megapixels resolution
• 5184x3456 pixels image size
• DIGIC 5 image processor
• Tethering cable and utility software
for remote capturing of images
Light sources in a laboratory setup is
added to illuminate the object and
eliminate the shadows and other noise.
32. Digital ImageAcquisition
• Fig. 3.4. Example RGB images of
sampled papaya fruits.
• Each row shows 10 RGB images
from different sides and orientation
of papaya fruits that are classified
into one of the six ripeness stages:
MS1 fruits in the first row, MS2 in the
second row and so on until MS6 or
overripe fruits in the last row.
• Based on [20] and due to
computation resources constraints,
the image size is reduced to 32 × 32
33. Hyperspectral Image
Acquisition
• A visible/near-infrared (VNIR)
hyperspectral imaging system
with 150 bands in between 470
nm to 900 nm wavelength data
range is used in this study.
• It is composed of an imaging
unit, active cooling system,
halogen light sources, a
platform, and a computer.
• It uses linescan sensor
technology to acquire HS data
through a push-broom
scanning approach.
• Figure 3.5 is shown here.
34. Hyperspectral Image
Acquisition
Figure 3.6. Hyperspectral data acquisition
and preprocessing.
(a) A 3D visualization of the raw HS data,
called a hyperspectral data cube, of a
papaya sample.
(b) A 2D lateral view of a HS image with
markers on the locations of the nine 32 ×
32 × 150 patches that were extracted
from the raw data cube [20], [59], [62] .
Apex Peduncle
35. Hyperspectral Image
Preprocessing
Clipping
• Outliers or extremely large
values are present in some
images due to the scene and
camera geometry [61]
• To remove the noise and to
keep the values within range
[0, 1]
Fig. 3.7. Histograms from a sample band of a HS image before (a) and after
clipping the extreme values (b).
36. Hyperspectral Image
Preprocessing
• The composition of images per
ripeness stage became 576 data
samples with MS1 label for both HSd
and RGB data sets, 666 with MS2
label, 792 with MS3 label, 720 for
MS4 label, 909 for MS5 label, and
945 for MS6 label as shown in Figure
3.8.
• The database comprises of 4,608
RGB images having three channels
and 4,608 HSd having 150 bands
within the range of 470 nm to 900 nm
wavelength. In this study, we have
produced a total of 9,216 data
entries of HSd and RGB with 32 × 32
pixel dimensions for each channel.
37. Hyperspectral Image
Preprocessing
• A further visualization on the
spectral patterns and RGB image
set can be observed in Figures 3.9
and 3.10.
• Ripeness levels are distinct in
550nm to 700nm range.
38. Hyperspectral Image
Preprocessing
• A further visualization on the
spectral patterns and RGB image
set can be observed in Figures 3.9
and 3.10.
• The line graphs of the mean and
median across the bands of each
patch coincides most of the time,
which means that our data is highly
symmetric and non-skewed.
Fig. 3.10. Mean and median of the reflectance values of the nine HSd
patches from a sample papaya fruit with MS1 label.
39. Computing Environment
• Python programming language,Anaconda,Tensorflow-GPU, Keras, and other
packages for deep learning model development
• Intel Core i5-9300H 2.40 GHz CPU (8 CPUs), NVIDIAGeForce GTX 1660Ti 6GB
GPU, and 8 GB memory space running onWindows 10 Home 64-bit (10.0, Build
18362) system
40. Performance Evaluation Metrics
• The multimodal models implemented in this dissertation are assessed in terms of
solution quality: precision, recall, top-2 error rate, accuracy, and F1 score, and
solution time: training time and prediction time.
• Accuracy refers to the percentage of samples that are correctly labeled among all
samples examined and is calculated as:
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = (𝑇𝑃 + 𝑇𝑁)/(𝑇𝑃 + 𝐹𝑃 + 𝐹𝑁 + 𝑇𝑁)
• Precision (P) tells us how precise or exact the predictions given by the classifier
are to the target or the correct one, while recall (R) prevents our model from
producing wastes as it quantitatively measures the proportion of samples that the
classifier identifies precisely with respect to other classes.
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃/(𝑇𝑃 + 𝐹𝑃)
𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃/(𝑇𝑃 + 𝐹𝑁)
41. Performance Evaluation Metrics
• F1-score, a robust metric frequently used in deep learning, measures the
harmonic mean of precision and recall by:
𝐹1 (𝑅, 𝑃) = 2𝑅𝑃/(𝑅 + 𝑃)
• Top-2 error rate measures the fraction of test samples in which the true label
(actual) is not among the top two classes predicted by the model.This is like the
top-1 and top-5 error rates that are used in the ImageNet LSVRC.
• In traditional sorting, farmers or sorting personnel categorize the fruit to the
ripeness stage that is either one class lower or higher than its correct ripeness
label, because there is only a minimal difference in appearances between
adjacent maturity or ripeness stages.
43. Multimodal Deep Learning Framework
Fig. 4.1. Multimodal deep learning framework for papaya fruit ripeness classification.
44. Multimodality via Feature
Concatenation (MDL-FC)
In this subsection, the study
(1) systematically implemented
and evaluated a multimodal deep
learning architecture, specifically
deep CNNs, with respect to
precision, recall, accuracy, top-2
error rate, F1-score, depth, and
number of parameters; and
(2) suggested a multimodal deep
CNN that acquires attributes
from two sensing systems,
specifically, an RGB image and a
hyperspectral reflectance data
cube. Figure 4.2. Multimodal deep learning via feature concatenation for non-
destructive papaya fruit ripeness estimation.
48. Multimodality via Feature Concatenation (MDL-FC)
Performance Results
Figure 4.5. Performance results of the preliminary training of a 2L CNN using (a) RGB dataset only and
(b) HS images dataset only.
49. Multimodality via Feature Concatenation (MDL-FC)
Performance Results
Figure 4.6. Performance results of the preliminary training of a 2L CNN with RGB+HS dataset for
(a) 100 and (b) 300 epochs.
53. Multimodality via Late
Fusion (MDL-LF)
In this subsection,
(1) a multimodal deep learning via
late fusion (MDL-LF)
framework for non-destructive
classification of papaya fruit
maturity using HS and RGB
images will be discussed,
(2) batch size (B), learning rate (lr),
and number of epochs (E)
parameter setup experiment
will be inspected, and
(3) the training and prediction
performance of deep CNNs
when integrated into the MDL-
LF architecture will be
examined.
Fig. 4.8. Multimodal deep learning via late fusion for non-destructive
papaya fruit ripeness estimation.
54. Multimodality via Late
Fusion (MDL-LF)
Multinomial logistic regression (MLR) as
meta-learner:
• can produce probabilities of multiple
independent variables belonging to
multiple categories
• makes no assumptions on normality
and linearity, and
• can select features that can
significantly improve classification
performance even when using
hyperspectral data [63].
55. Multimodality via Late
Fusion (MDL-LF)
Multinomial logistic regression (MLR)
learns from a set of K weight vectors
and biases:
𝑝 𝐶𝑘 θ = 𝑦𝑘 θ =
exp(𝑎𝑘)
𝑗 exp(𝑎𝑗)
where 𝑎𝑘 = 𝑤𝑘
𝑇
𝜃 + 𝑏𝑘 is the activation
function for k = 1,.., K classes,
𝑤𝑘 = 𝑤𝑘1, 𝑤𝑘2, … , 𝑤𝑘𝑀
𝑇
for M
input variables,
𝜃 = 𝜃1, 𝜃2, … , 𝜃𝑀
𝑇
,
𝐶𝑘 represents the 1-of-k scheme for
each class k.
Fig. 4.9. Multinomial logistic regression implementation structure as
meta-learner of the MDL-LF framework.
𝑎𝑘 = 𝑤𝑘
𝑇
𝑥𝑟 + 𝑤𝑘
𝑇
𝑥ℎ + 𝑏𝑘
60. Multimodality via End-to-End
Deep CNN
In this subsection,
(1) novel deep CNNs based on
MDL-LF framework using
two modalities, which are HS
and RGB images, will be
explained, and
(2) the training and prediction
performance of these novel
deep CNNs for non-
destructive classification of
papaya fruit ripeness will be
examined. Fig 4.13. End-to-end deep CNN implementation of MDL-LF.
(50 bands)
61. Multimodality via End-to-End
Deep CNN
• Seven E2E Deep CNN were
built for this experiment.
• The blocking characteristic of
VGG architecture and its
utilization of 3x3 kernels is
added in these models; 2x2
kernels are also explored to
allow increasing the network’s
depth.
• Example graphical plot on the
right.
63. Multimodality via End-to-End
Deep CNN
• Seven E2E Deep CNN were
built for this experiment.
• Performance graphs of the
top-performing MD-CNNs:
(Fig. 4.14.b) 3B5L MD-CNN
(top), and (Fig. 4.14.d) 6B5L
MD-CNN (2x2 kernel)
(bottom).
• Consistent training and
validation loss patterns
64. Multimodality via End-to-End
Deep CNN
• Seven E2E Deep CNN were
built for this experiment.
• Confusion matrices of the top-
performing MD-CNNs: (Fig.
4.14.b) 3B5L MD-CNN (left),
and (Fig. 4.14.d) 6B5L MD-
CNN (2x2 kernel) (right).
• Relatively high top-2 accuracy
68. Conclusion
• The applications of deep learning and machine learning are
being used in a variety of fields, including but not limited to
precision and smart agriculture, nutrition, food safety and
quality assurance.
• Estimating a fruit's maturity or ripeness level can help assess
the vitamins, minerals, and other nutrients that are altered in
the fruit ripening.
• However, accurately classifying a papaya fruit based on the
six ripeness stages standard remains a challenge since most
changes happen internally rather than to the external
characteristics.
• Using internal properties in classification would require
destructive and time-consuming laboratory tests.
69. Conclusion
• With the emergence of deep learning and imaging
technologies, data with high dimensions, which correlates
with internal and external characteristics of an object such as
those produced by hyperspectral cameras, can be processed
to perform a high-level intelligent classification task without
impairing the fruit.
• In fruit maturity classification, specifically for Papaya fruit,
we have introduced two main multimodal deep learning
framework using feature concatenation and late fusion
algorithms that were implemented in this study.
• A hyperspectral imaging and a digital imaging system were
used to acquire hyperspectral data cubes and RGB images of
papaya fruit samples, respectively, at six ripeness stages
from unripe stage to overripe stage.This established a new
multimodal raw data set—a major contribution of this study.
70. Conclusion
• Both the feature concatenation and late fusion approaches
demonstrated very outstanding results in papaya fruit
ripeness classification
• Feature Concatenation:
• MD-VGG16 achieved F1-score = 0.90
• Late Fusion:
• VGG16-AlexNet-MLR obtained F1-score = 0.97
• 8L End-to-End MD-CNN produced F1-score = 0.97
71. Conclusion
• The proposed methods are quite promising for this type of
application, according to the results.
• However, there are still significant issues that the
hyperspectral classification research community must
address. Because of the high dimensionality of the data
generated by hyperspectral imaging, the number of
experimentations that may be performed is limited
depending on the computational resources.
• The cost of the imaging equipment is also a major issue,
particularly when adopting the technology for agricultural
purposes; low-cost hyperspectral imaging is now gaining
attention in research and development.
72. Conclusion
• In general, multimodality through imaging systems
combined with advanced deep CNN models can non-
destructively classify fruit ripeness even at finer levels such as
six ripeness stages.
• The findings in this research demonstrated the potential of
multimodal imaging systems and multimodal deep learning
in real-time classification of fruit ripeness in the production
site.
This is the key advantage of deep CNN in multimodality implementation of this study.
This is the key advantage of deep CNN in multimodality implementation of this study.
This is the key advantage of deep CNN in multimodality implementation of this study.
This is the key advantage of deep CNN in multimodality implementation of this study.
This is the key advantage of deep CNN in multimodality implementation of this study.
This is the key advantage of deep CNN in multimodality implementation of this study.
In traditional sorting, farmers or sorting personnel categorize the fruit to the ripeness stage that is either one class lower or higher than its correct ripeness label, because there is only a minimal difference in appearances between adjacent maturity or ripeness stages.
Moreover, since this study involved trial and investigation, observing the models’ behavior by computing and recording the top-2 error rate should be implemented. This will lead to a greater understanding of the performance of multimodal deep learning algorithms in the agricultural sector.
This architecture consists of 8 layers in total: 5 of which are convolutional layers namely Conv1, Conv2, …, and Conv5. Conv1 and Conv2 (first two layers are connected to maxpooling layers to extract the maximum number of features). This also works with Conv4, while Conv5 is connected to the fully-con layers… all output are connected to the ReLU non-linear activation function (max function, sets nega vals to 0). Final layer is a Softmax activation layer, maps out each output into probabilities that sums up to 1.
In traditional sorting, farmers or sorting personnel categorize the fruit to the ripeness stage that is either one class lower or higher than its correct ripeness label, because there is only a minimal difference in appearances between adjacent maturity or ripeness stages.
Moreover, since this study involved trial and investigation, observing the models’ behavior by computing and recording the top-2 error rate should be implemented. This will lead to a greater understanding of the performance of multimodal deep learning algorithms in the agricultural sector.
In traditional sorting, farmers or sorting personnel categorize the fruit to the ripeness stage that is either one class lower or higher than its correct ripeness label, because there is only a minimal difference in appearances between adjacent maturity or ripeness stages.
Moreover, since this study involved trial and investigation, observing the models’ behavior by computing and recording the top-2 error rate should be implemented. This will lead to a greater understanding of the performance of multimodal deep learning algorithms in the agricultural sector.
In traditional sorting, farmers or sorting personnel categorize the fruit to the ripeness stage that is either one class lower or higher than its correct ripeness label, because there is only a minimal difference in appearances between adjacent maturity or ripeness stages.
Moreover, since this study involved trial and investigation, observing the models’ behavior by computing and recording the top-2 error rate should be implemented. This will lead to a greater understanding of the performance of multimodal deep learning algorithms in the agricultural sector.
In traditional sorting, farmers or sorting personnel categorize the fruit to the ripeness stage that is either one class lower or higher than its correct ripeness label, because there is only a minimal difference in appearances between adjacent maturity or ripeness stages.
Moreover, since this study involved trial and investigation, observing the models’ behavior by computing and recording the top-2 error rate should be implemented. This will lead to a greater understanding of the performance of multimodal deep learning algorithms in the agricultural sector.
In traditional sorting, farmers or sorting personnel categorize the fruit to the ripeness stage that is either one class lower or higher than its correct ripeness label, because there is only a minimal difference in appearances between adjacent maturity or ripeness stages.
Moreover, since this study involved trial and investigation, observing the models’ behavior by computing and recording the top-2 error rate should be implemented. This will lead to a greater understanding of the performance of multimodal deep learning algorithms in the agricultural sector.