Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Applications of information technology in agriculture ws ns for environmental...Aboul Ella Hassanien
This presentation due the workshop at faculty of agriculture - Suss Canal University organized by scientific research group in Egypt (SRGE) on Tuesday 8 April 214
Agricultural Informatics is a valuable domain in the field of interdisciplinary sciences. This is responsible for the applications of Information Technology, Computing and similar technologies into the agricultural activities. This is the combination of Agricultural Science and Information Sciences. The field due to technological nature is much closed with the Agricultural Engineering or Agricultural Technology. There are many allied and similar nomenclature of the fields but all of these are primarily responsible for the same purpose. The field is rapidly increasing in recent past and most practiced in the developed nation. However, in developing countries as well Agricultural Informatics becomes an emerging field of practice and growing rapidly. Agricultural Informatics is growing both in pre and post agricultural activity. This branch is considered as branch of Information Sciences & Technology due to its technological applications in the field of agriculture and allied areas. Information Sciences are the broadest field within the allied branches and growing rapidly. Agricultural Informatics educational programs have started in recent past in different level and stream of education viz. science and technology. However within the broad periphery of Information Sciences it could be offered in other streams and under the wide variety of Information Sciences. This paper is broad and interdisciplinary in nature and deals with the aspects of the Information Sciences and Technology including features, nature, scope and also the potentialities in respect of Agricultural Informatics.
APPLICATION OF ARTIFICIAL INTELLIGENCE TO TRACK PLANT DISEASESABHISEK RATH
this explained how artificial intelligence can be used in agriculture and especially in plant pathology i.e., tracking plant diseases, use of robotics, drone in applying chemicals and other aspects.
IRJET- An Android based Image Processing Application to Detect Plant DiseaseIRJET Journal
This document describes an Android application developed to detect plant diseases using image processing techniques. The application allows users to capture images of plant leaves using their phone's camera or select images from storage. It then analyzes the images to detect any infected spots or areas. It identifies disease occurrences and calculates the percentage of the plant affected. The application was created in Android Studio using Java and utilizes techniques like color transformation, edge detection, and k-means clustering for image analysis and classification. It aims to help farmers detect diseases in crops early in order to properly manage plant health.
Automated Crop Inspection and Pest Control Using Image ProcessingIJERDJOURNAL
ABSTRACT: Agriculture is the backbone of our country. India is an agricultural country where the most of the population depends on agriculture. Research in agriculture is aimed towards increasing productivity and profit. There are several automated systems available in literature, which are developed for irrigation control and environmental monitoring in the field. However, it is essential to monitor the plant growth stage by stage and take decisions accordingly. In addition to monitoring the environmental parameters such as pH, moisture content and temperature, it is inevitable to identify the onset of plant diseases too. It is the key to prevent the losses in yield and quantity of agricultural product. Plant disease identification by continuous visual monitoring is very difficult task to farmers and at the same time it is less accurate and can be done in limited areas. Hence this projects aims at developing an image processing algorithm to identify the diseases in rice plant. Rice blast disease occurring in rice plant is due to magnaporthe grisea and this disease also occurs in wheat, rye, barley, pearl and millet. Due to rice blast disease, 60 million people are affected in 85 countries worldwide. Image processing technique is adopted as it is more accurate. Early disease detection can increase the crop production by inducing proper pesticide usage.
This document outlines a proposed plant leaf disease detection system using image processing on Android mobile phones. The system aims to help farmers easily and cost-effectively detect plant diseases, identify severity levels, and receive treatment suggestions. It will use algorithms like blob detection and HSV color modeling to analyze leaf images and determine diseases. The Android app is intended to provide an affordable solution to identify a variety of disease types and inform farmers in their local language.
Smart Fruit Classification using Neural Networksijtsrd
The objective of this project is to develop a system that helps the food industry to classify fruits based on specific quality features. Our system will give best performance when used to sort some brand of fruits. The fruit industry plays a vital role in a countrys economic growth. They account for a fraction of the agricultural output produced by a country. It forms a part of the food processing industry. Fruits are a major source of energy, vitamins, minerals, fiber and other nutrients. They contribute to an essential part of our diet. Fruits come in varying shapes, color and sizes. Some of them are exported, thereby yielding profit to the industry. K. Sandhiya | M. Vidhya | M. Shivaranjani | S. Saranya"Smart Fruit Classification using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd6986.pdf http://www.ijtsrd.com/engineering/computer-engineering/6986/smart-fruit-classification-using-neural-networks/k-sandhiya
Fruit Disease Detection and ClassificationIRJET Journal
This document proposes and experimentally validates a solution for detecting and classifying fruit diseases from images. The proposed approach uses K-means clustering for image segmentation, extracts features from the segmented image, and classifies the images using a Support Vector Machine (SVM). The experimental results show the proposed solution can accurately detect and automatically classify fruit diseases. It is intended to help farmers identify diseases early to improve crop management and reduce economic losses from diseases.
Applications of information technology in agriculture ws ns for environmental...Aboul Ella Hassanien
This presentation due the workshop at faculty of agriculture - Suss Canal University organized by scientific research group in Egypt (SRGE) on Tuesday 8 April 214
Agricultural Informatics is a valuable domain in the field of interdisciplinary sciences. This is responsible for the applications of Information Technology, Computing and similar technologies into the agricultural activities. This is the combination of Agricultural Science and Information Sciences. The field due to technological nature is much closed with the Agricultural Engineering or Agricultural Technology. There are many allied and similar nomenclature of the fields but all of these are primarily responsible for the same purpose. The field is rapidly increasing in recent past and most practiced in the developed nation. However, in developing countries as well Agricultural Informatics becomes an emerging field of practice and growing rapidly. Agricultural Informatics is growing both in pre and post agricultural activity. This branch is considered as branch of Information Sciences & Technology due to its technological applications in the field of agriculture and allied areas. Information Sciences are the broadest field within the allied branches and growing rapidly. Agricultural Informatics educational programs have started in recent past in different level and stream of education viz. science and technology. However within the broad periphery of Information Sciences it could be offered in other streams and under the wide variety of Information Sciences. This paper is broad and interdisciplinary in nature and deals with the aspects of the Information Sciences and Technology including features, nature, scope and also the potentialities in respect of Agricultural Informatics.
APPLICATION OF ARTIFICIAL INTELLIGENCE TO TRACK PLANT DISEASESABHISEK RATH
this explained how artificial intelligence can be used in agriculture and especially in plant pathology i.e., tracking plant diseases, use of robotics, drone in applying chemicals and other aspects.
IRJET- An Android based Image Processing Application to Detect Plant DiseaseIRJET Journal
This document describes an Android application developed to detect plant diseases using image processing techniques. The application allows users to capture images of plant leaves using their phone's camera or select images from storage. It then analyzes the images to detect any infected spots or areas. It identifies disease occurrences and calculates the percentage of the plant affected. The application was created in Android Studio using Java and utilizes techniques like color transformation, edge detection, and k-means clustering for image analysis and classification. It aims to help farmers detect diseases in crops early in order to properly manage plant health.
Automated Crop Inspection and Pest Control Using Image ProcessingIJERDJOURNAL
ABSTRACT: Agriculture is the backbone of our country. India is an agricultural country where the most of the population depends on agriculture. Research in agriculture is aimed towards increasing productivity and profit. There are several automated systems available in literature, which are developed for irrigation control and environmental monitoring in the field. However, it is essential to monitor the plant growth stage by stage and take decisions accordingly. In addition to monitoring the environmental parameters such as pH, moisture content and temperature, it is inevitable to identify the onset of plant diseases too. It is the key to prevent the losses in yield and quantity of agricultural product. Plant disease identification by continuous visual monitoring is very difficult task to farmers and at the same time it is less accurate and can be done in limited areas. Hence this projects aims at developing an image processing algorithm to identify the diseases in rice plant. Rice blast disease occurring in rice plant is due to magnaporthe grisea and this disease also occurs in wheat, rye, barley, pearl and millet. Due to rice blast disease, 60 million people are affected in 85 countries worldwide. Image processing technique is adopted as it is more accurate. Early disease detection can increase the crop production by inducing proper pesticide usage.
This document outlines a proposed plant leaf disease detection system using image processing on Android mobile phones. The system aims to help farmers easily and cost-effectively detect plant diseases, identify severity levels, and receive treatment suggestions. It will use algorithms like blob detection and HSV color modeling to analyze leaf images and determine diseases. The Android app is intended to provide an affordable solution to identify a variety of disease types and inform farmers in their local language.
Smart Fruit Classification using Neural Networksijtsrd
The objective of this project is to develop a system that helps the food industry to classify fruits based on specific quality features. Our system will give best performance when used to sort some brand of fruits. The fruit industry plays a vital role in a countrys economic growth. They account for a fraction of the agricultural output produced by a country. It forms a part of the food processing industry. Fruits are a major source of energy, vitamins, minerals, fiber and other nutrients. They contribute to an essential part of our diet. Fruits come in varying shapes, color and sizes. Some of them are exported, thereby yielding profit to the industry. K. Sandhiya | M. Vidhya | M. Shivaranjani | S. Saranya"Smart Fruit Classification using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd6986.pdf http://www.ijtsrd.com/engineering/computer-engineering/6986/smart-fruit-classification-using-neural-networks/k-sandhiya
Fruit Disease Detection and ClassificationIRJET Journal
This document proposes and experimentally validates a solution for detecting and classifying fruit diseases from images. The proposed approach uses K-means clustering for image segmentation, extracts features from the segmented image, and classifies the images using a Support Vector Machine (SVM). The experimental results show the proposed solution can accurately detect and automatically classify fruit diseases. It is intended to help farmers identify diseases early to improve crop management and reduce economic losses from diseases.
Artificial intelligence ,robotics and cfd by sneha gaurkar Sneha Gaurkar
The document discusses artificial intelligence, robotics, and computational fluid dynamics. It provides introductions and definitions for each topic, as well as descriptions of their applications in areas like pharmaceutical manufacturing and drug discovery. It also outlines some advantages and challenges of adopting AI technologies in the pharmaceutical industry, such as reducing errors but also challenges around data quality and changing traditional practices. The document takes an overview approach to these emerging fields.
Classification of Apple diseases through machine learningMuqaddas Bin Tahir
This presentation describes a research work in which constitutional neural network is used for fruit’s classification and recognizing their diseases. CNN is the popular , advanced and powerful architecture of Neural Network. The method describe in this presentation perform better than other classification and recognition techniques on various datasets and it is not affected by illumination, translation and occlusion problems.
This document provides an overview of artificial intelligence and robotics in the pharmaceutical industry. It discusses how AI and robotics are being used for pharmaceutical automation to improve accuracy, reliability, and reduce human error. Applications include use in laboratories for tests, packaging lines, and quality assurance. Challenges include high costs, lack of skilled workers, and ensuring safety. The future of AI and robotics in pharmaceuticals includes using machine learning to accelerate drug discovery and development through analysis of large datasets. Nano-robots that can repair cells without damage also show promise.
A mobile application which can save time, effort and money by giving technical video explanation based on the recognized image for learners. The collected video information contains all the detailed explanation of the scanned image done by the user. This application groups Augmented reality , 3d objects , sound , video , AI for image recognition into an easy and compact application for the benefit of the every learner. This web application is built C#, UNITY, VUFORIA with the help of Vuforia server connected by asserts. This mobile application serves as an easier way to access details and documentaries related to recognized object. It includes an automatic AR Camera and image target along with the video player which makes it easier for recognizing the image and to learn. The database is done with Vuforia cloud database. The UI provides an excellent user friendly environment that makes the communication more interactive.
IRJET - Disease Detection in Plant using Machine LearningIRJET Journal
This document discusses using machine learning and image processing techniques to detect diseases in plants. The proposed system utilizes convolutional neural networks (CNNs) to classify plant images as either healthy or diseased based on features extracted from the images. The system architecture includes preprocessing the images, extracting color and texture features, running the features through a CNN model for classification training and testing, and outputting whether plants are normal or abnormal. The goal is to help farmers automatically detect plant diseases early on by analyzing images of plant leaves.
IRJET- The Future of Farming through the IoT PerspectiveIRJET Journal
The document discusses how Internet of Things (IoT) technologies can help address problems in traditional Indian farming. It presents a literature review of previous work on applying IoT sensors and devices to areas like irrigation control, environmental monitoring, and precision agriculture. The research gap identified is the lack of a system to provide farmers with real-time information on suitable farming conditions. An architecture is proposed to collect unstructured sensor data, process it into a structured format, apply data mining techniques to extract useful insights, and make this information available to farmers to help predict optimal times for activities like planting and irrigation. This could help increase productivity by allowing farmers to make decisions based on accurate, real-time data rather than traditional methods.
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
This document describes a project on plant disease detection and classification using deep learning. The objectives are to automatically detect plant diseases as early as symptoms appear on leaves in order to increase crop productivity. Deep learning techniques like convolutional neural networks (CNNs) are implemented using libraries like TensorFlow and Keras. Two CNN models, VGG16 and VGG19, are compared for classifying diseases in a dataset of 38 classes and 87k images of 14 crop species. The system achieved over 95% accuracy on validation. Future work involves developing a mobile app and integrating disease recommendations to help farmers.
IRJET- Applications of Internet of Things in Human LifeIRJET Journal
This document discusses applications of the Internet of Things (IoT) in human life. It begins with an introduction to IoT, defining it as a network of physical objects with sensors that can communicate and share data. It then reviews literature on IoT applications and challenges. Several applications are described in detail, including smart homes, healthcare, agriculture, and smart grids. The document concludes that while IoT has many applications in daily life, challenges associated with each application must be considered to ensure safety and security.
Determination of Various Diseases in Two Most Consumed Fruits using Artificia...ijtsrd
Fruit diseases are manifested by deformations during or after harvesting the components in the fruit, when the infestation is caused by spores, fungi, insects or other contaminants. In early agricultural practices, it is thought that non destructive examination is possible with the analysis of pre harvest fruit leaves and early diagnosis of the disease, while post harvest detection and classification of fruit disease is possible by evaluating simple image processing techniques. Diseases of rotten or stained fruits without destruction. In this way, the disease will be identified and classified and the awareness of the producer for the next harvest will be provided. For this purpose, studies were carried out with apple and quince fruit, images were determined using still fruit pictures and machine learning, and disease classification was provided with labels. Image processing techniques are a system that detects disease made to a real time camera and prints it on the screen. Within the scope of this study, the data set was created and images of 22 apples and 18 quinces were taken. The image was classified by similarities in the literature review. The success of the proposed Convolutional Neural Network architecture in recognizing the disease was evaluated. By comparing the trained network, AlexNet architecture, with the proposed architecture, it has been determined that the success of image recognition has increased with the proposed method. Aysun Yilmaz Kizilboga | Atilla Ergüzen | Erdal Erdal "Determination of Various Diseases in Two Most Consumed Fruits using Artificial Neural Networks and Deep Learning Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38128.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38128/determination-of-various-diseases-in-two-most-consumed-fruits-using-artificial-neural-networks-and-deep-learning-techniques/aysun-yilmaz-kizilboga
A wearable device for machine learning based elderly's activity tracking and ...journalBEEI
The number of older people is increasing in many countries. By 2030, it is estimated that 15% of the overall population will be comprised of people aged 65 and above. Hence, the monitoring and tracking of elder activities to ensure they live an active life has become a major research topic in recent years. In this work, an elderly sub-activity tracking system is developed to detect the sub-activity of the elderly based on their physical activities and indoor location. The physical activities tracking system and indoor location system is combined in this project to enhance the context of the elderly activities (i.e. sub-activities as defined in this project). An indoor location system is developed by using Bluetooth Low Energy (BLE) beacon and BLE scanners to measure the Received Signal Strength Indicator (RSSI) signal to detect the location of the elderly. The activity tracking is carried out via a waist wearable device worn by the elderly. Random forest and Support Vector Machine (SVM) are used as machine learning classifiers to predict the activity and indoor location with an accuracy of 95.03% and 86.58%, respectively. The data from activity tracking and indoor location sub-systems will then be combined to derive the sub-activity and push to an online Internet of Things (IoT) platform for remote monitoring and notification.
Artificial intelligence has the potential to accelerate drug discovery by generating new molecular structures, automatically designing drug candidates, and using historical data to identify treatments. Companies are using AI techniques like deep learning and generative adversarial networks to analyze vast amounts of data to propose new drug candidates. Robots are also being used in pharmaceutical laboratories and manufacturing to perform repetitive and precise tasks, allowing researchers to focus on higher-level work. Computational fluid dynamics is another tool being used to analyze and optimize pharmaceutical processes.
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...Wesley De Neve
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to Smart Farming. Presentation given at the Korea-Europe International Conference on the 4th Industry Revolution.
IRJET- Medicine Information Retrieval Application- PharmaguideIRJET Journal
This document describes an Android-based mobile application called PharmaGuide that allows users to retrieve information about medicines. The application's key features include allowing users to scan images of medicines using optical character recognition to obtain the name, usage, and side effects from a database. It also enables users to purchase medicines online that will be delivered to their homes. The application was developed using Android Studio and utilizes a SQL database to store medicine information. It aims to make it more convenient for people to learn about medicines and order them in one centralized mobile application.
Clinical Trial Design and Artificial Intelligence | Pepgra.comPEPGRA Healthcare
Clinical trials take up the last half of the 10 – 15 year, 1.5 – 2.0 billion USD, cycle of development just for introducing a new drug within a market.
1. AI and its Evolution
2. AI in Clinical Trials
To Continue Reading: https://bit.ly/2W01UDQ
Contact Us:
Website : https://bit.ly/33Fwsye
Email us: sales.cro@pepgra.com
Whatsapp: +91 9884350006
BEHAVIOR-BASED SECURITY FOR MOBILE DEVICES USING MACHINE LEARNING TECHNIQUESijaia
The goal of this research project is to design and implement a mobile application and machine learning techniques to solve problems related to the security of mobile devices. We introduce in this paper a behavior-based approach that can be applied in a mobile environment to capture and learn the behavior of
mobile users. The proposed system was tested using Android OS and the initial experimental results show that the proposed technique is promising, and it can be used effectively to solve the problem of anomaly detection in mobile devices.
This document describes a proposed system to detect plant diseases using machine learning and provide remedial measures. It will use a mobile app to classify plant leaf images using a TensorFlow Lite model trained with InceptionV3. The model will identify the disease and fetch details like treatment from a database to display to the user. This aims to make plant disease detection and treatment advice more easily accessible compared to existing computer-based systems.
IRJET- Applications of Internet of Things in Human LifeIRJET Journal
This document discusses applications of the Internet of Things (IoT) in human life. It first provides background on IoT, defining it as a network of physical objects embedded with sensors that can communicate and share data. The document then reviews several applications of IoT, including smart grids, data analysis, agriculture, and healthcare. Specifically, it describes how IoT can be used to remotely monitor energy use and integrate renewable sources in smart grids, analyze sensor and social media data, automate irrigation and monitor farm conditions, and remotely track health metrics. The document concludes that while IoT brings many benefits, challenges around its applications need to be addressed.
Artificial intelligence ,robotics and cfd by sneha gaurkar Sneha Gaurkar
The document discusses artificial intelligence, robotics, and computational fluid dynamics. It provides introductions and definitions for each topic, as well as descriptions of their applications in areas like pharmaceutical manufacturing and drug discovery. It also outlines some advantages and challenges of adopting AI technologies in the pharmaceutical industry, such as reducing errors but also challenges around data quality and changing traditional practices. The document takes an overview approach to these emerging fields.
Classification of Apple diseases through machine learningMuqaddas Bin Tahir
This presentation describes a research work in which constitutional neural network is used for fruit’s classification and recognizing their diseases. CNN is the popular , advanced and powerful architecture of Neural Network. The method describe in this presentation perform better than other classification and recognition techniques on various datasets and it is not affected by illumination, translation and occlusion problems.
This document provides an overview of artificial intelligence and robotics in the pharmaceutical industry. It discusses how AI and robotics are being used for pharmaceutical automation to improve accuracy, reliability, and reduce human error. Applications include use in laboratories for tests, packaging lines, and quality assurance. Challenges include high costs, lack of skilled workers, and ensuring safety. The future of AI and robotics in pharmaceuticals includes using machine learning to accelerate drug discovery and development through analysis of large datasets. Nano-robots that can repair cells without damage also show promise.
A mobile application which can save time, effort and money by giving technical video explanation based on the recognized image for learners. The collected video information contains all the detailed explanation of the scanned image done by the user. This application groups Augmented reality , 3d objects , sound , video , AI for image recognition into an easy and compact application for the benefit of the every learner. This web application is built C#, UNITY, VUFORIA with the help of Vuforia server connected by asserts. This mobile application serves as an easier way to access details and documentaries related to recognized object. It includes an automatic AR Camera and image target along with the video player which makes it easier for recognizing the image and to learn. The database is done with Vuforia cloud database. The UI provides an excellent user friendly environment that makes the communication more interactive.
IRJET - Disease Detection in Plant using Machine LearningIRJET Journal
This document discusses using machine learning and image processing techniques to detect diseases in plants. The proposed system utilizes convolutional neural networks (CNNs) to classify plant images as either healthy or diseased based on features extracted from the images. The system architecture includes preprocessing the images, extracting color and texture features, running the features through a CNN model for classification training and testing, and outputting whether plants are normal or abnormal. The goal is to help farmers automatically detect plant diseases early on by analyzing images of plant leaves.
IRJET- The Future of Farming through the IoT PerspectiveIRJET Journal
The document discusses how Internet of Things (IoT) technologies can help address problems in traditional Indian farming. It presents a literature review of previous work on applying IoT sensors and devices to areas like irrigation control, environmental monitoring, and precision agriculture. The research gap identified is the lack of a system to provide farmers with real-time information on suitable farming conditions. An architecture is proposed to collect unstructured sensor data, process it into a structured format, apply data mining techniques to extract useful insights, and make this information available to farmers to help predict optimal times for activities like planting and irrigation. This could help increase productivity by allowing farmers to make decisions based on accurate, real-time data rather than traditional methods.
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
This document describes a project on plant disease detection and classification using deep learning. The objectives are to automatically detect plant diseases as early as symptoms appear on leaves in order to increase crop productivity. Deep learning techniques like convolutional neural networks (CNNs) are implemented using libraries like TensorFlow and Keras. Two CNN models, VGG16 and VGG19, are compared for classifying diseases in a dataset of 38 classes and 87k images of 14 crop species. The system achieved over 95% accuracy on validation. Future work involves developing a mobile app and integrating disease recommendations to help farmers.
IRJET- Applications of Internet of Things in Human LifeIRJET Journal
This document discusses applications of the Internet of Things (IoT) in human life. It begins with an introduction to IoT, defining it as a network of physical objects with sensors that can communicate and share data. It then reviews literature on IoT applications and challenges. Several applications are described in detail, including smart homes, healthcare, agriculture, and smart grids. The document concludes that while IoT has many applications in daily life, challenges associated with each application must be considered to ensure safety and security.
Determination of Various Diseases in Two Most Consumed Fruits using Artificia...ijtsrd
Fruit diseases are manifested by deformations during or after harvesting the components in the fruit, when the infestation is caused by spores, fungi, insects or other contaminants. In early agricultural practices, it is thought that non destructive examination is possible with the analysis of pre harvest fruit leaves and early diagnosis of the disease, while post harvest detection and classification of fruit disease is possible by evaluating simple image processing techniques. Diseases of rotten or stained fruits without destruction. In this way, the disease will be identified and classified and the awareness of the producer for the next harvest will be provided. For this purpose, studies were carried out with apple and quince fruit, images were determined using still fruit pictures and machine learning, and disease classification was provided with labels. Image processing techniques are a system that detects disease made to a real time camera and prints it on the screen. Within the scope of this study, the data set was created and images of 22 apples and 18 quinces were taken. The image was classified by similarities in the literature review. The success of the proposed Convolutional Neural Network architecture in recognizing the disease was evaluated. By comparing the trained network, AlexNet architecture, with the proposed architecture, it has been determined that the success of image recognition has increased with the proposed method. Aysun Yilmaz Kizilboga | Atilla Ergüzen | Erdal Erdal "Determination of Various Diseases in Two Most Consumed Fruits using Artificial Neural Networks and Deep Learning Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38128.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38128/determination-of-various-diseases-in-two-most-consumed-fruits-using-artificial-neural-networks-and-deep-learning-techniques/aysun-yilmaz-kizilboga
A wearable device for machine learning based elderly's activity tracking and ...journalBEEI
The number of older people is increasing in many countries. By 2030, it is estimated that 15% of the overall population will be comprised of people aged 65 and above. Hence, the monitoring and tracking of elder activities to ensure they live an active life has become a major research topic in recent years. In this work, an elderly sub-activity tracking system is developed to detect the sub-activity of the elderly based on their physical activities and indoor location. The physical activities tracking system and indoor location system is combined in this project to enhance the context of the elderly activities (i.e. sub-activities as defined in this project). An indoor location system is developed by using Bluetooth Low Energy (BLE) beacon and BLE scanners to measure the Received Signal Strength Indicator (RSSI) signal to detect the location of the elderly. The activity tracking is carried out via a waist wearable device worn by the elderly. Random forest and Support Vector Machine (SVM) are used as machine learning classifiers to predict the activity and indoor location with an accuracy of 95.03% and 86.58%, respectively. The data from activity tracking and indoor location sub-systems will then be combined to derive the sub-activity and push to an online Internet of Things (IoT) platform for remote monitoring and notification.
Artificial intelligence has the potential to accelerate drug discovery by generating new molecular structures, automatically designing drug candidates, and using historical data to identify treatments. Companies are using AI techniques like deep learning and generative adversarial networks to analyze vast amounts of data to propose new drug candidates. Robots are also being used in pharmaceutical laboratories and manufacturing to perform repetitive and precise tasks, allowing researchers to focus on higher-level work. Computational fluid dynamics is another tool being used to analyze and optimize pharmaceutical processes.
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...Wesley De Neve
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to Smart Farming. Presentation given at the Korea-Europe International Conference on the 4th Industry Revolution.
IRJET- Medicine Information Retrieval Application- PharmaguideIRJET Journal
This document describes an Android-based mobile application called PharmaGuide that allows users to retrieve information about medicines. The application's key features include allowing users to scan images of medicines using optical character recognition to obtain the name, usage, and side effects from a database. It also enables users to purchase medicines online that will be delivered to their homes. The application was developed using Android Studio and utilizes a SQL database to store medicine information. It aims to make it more convenient for people to learn about medicines and order them in one centralized mobile application.
Clinical Trial Design and Artificial Intelligence | Pepgra.comPEPGRA Healthcare
Clinical trials take up the last half of the 10 – 15 year, 1.5 – 2.0 billion USD, cycle of development just for introducing a new drug within a market.
1. AI and its Evolution
2. AI in Clinical Trials
To Continue Reading: https://bit.ly/2W01UDQ
Contact Us:
Website : https://bit.ly/33Fwsye
Email us: sales.cro@pepgra.com
Whatsapp: +91 9884350006
BEHAVIOR-BASED SECURITY FOR MOBILE DEVICES USING MACHINE LEARNING TECHNIQUESijaia
The goal of this research project is to design and implement a mobile application and machine learning techniques to solve problems related to the security of mobile devices. We introduce in this paper a behavior-based approach that can be applied in a mobile environment to capture and learn the behavior of
mobile users. The proposed system was tested using Android OS and the initial experimental results show that the proposed technique is promising, and it can be used effectively to solve the problem of anomaly detection in mobile devices.
This document describes a proposed system to detect plant diseases using machine learning and provide remedial measures. It will use a mobile app to classify plant leaf images using a TensorFlow Lite model trained with InceptionV3. The model will identify the disease and fetch details like treatment from a database to display to the user. This aims to make plant disease detection and treatment advice more easily accessible compared to existing computer-based systems.
IRJET- Applications of Internet of Things in Human LifeIRJET Journal
This document discusses applications of the Internet of Things (IoT) in human life. It first provides background on IoT, defining it as a network of physical objects embedded with sensors that can communicate and share data. The document then reviews several applications of IoT, including smart grids, data analysis, agriculture, and healthcare. Specifically, it describes how IoT can be used to remotely monitor energy use and integrate renewable sources in smart grids, analyze sensor and social media data, automate irrigation and monitor farm conditions, and remotely track health metrics. The document concludes that while IoT brings many benefits, challenges around its applications need to be addressed.
Muhammad The Messenger of the One and Only Godm_aljukh
The document discusses the life and achievements of Muhammad (peace be upon him) through quotes from various prominent historical figures from different faiths and backgrounds. It highlights how Muhammad revolutionized societies and nations through his teachings, established laws and moral codes, and was able to accomplish things no other man has achieved. Many scholars and statesmen expressed that Muhammad must have been following a divine mission to have been able to inspire such change and unity across the world in his lifetime.
Den här presentationen använde jag första gången i Östersund 19 november och Härnösand 20 november 2013. Uppdragsgivare: Härnösands stift i Svenska Kyrkan
حقاً؟!.. إذن.. هل تعرف ما هي الحاسة السادسة؟!..
يقال إنها توجد عند بعض الناس..
الذين يتمكنون من توقع بعض الأحداث قبل حدوثها..
مثل الذي قام بإلغاء حجزه على طائرة لشعوره بانقباض في صدره!!..
و ترحل الطائرة التي كانت طائرته..
Enligt Högskoleverket har fler än 20 procent av svenska studenter inget jobb ett år efter sin examen. Att nyutexaminerade studenter inte får relevanta jobb direkt efter examen är ett slöseri med resurser.
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This document discusses NoSQL databases and provides an overview of several popular NoSQL database types. It explains that NoSQL databases offer flexible schemas, join-less querying, and horizontal scalability. It also discusses the CAP theorem and how NoSQL databases achieve different levels of consistency and availability. Finally, it provides brief descriptions of several NoSQL databases like MongoDB, CouchDB, Redis, and Neo4j and their typical use cases.
The Shapiro Group surveyed 2000 Americans and found that if consumers know a small business is a Chamber member, the business sees a 44% increase in favorability, 51% increase in awareness, and 63% increase in likelihood of patronage. The document then lists various marketing opportunities for Chamber members, including website listings and ads, as well as testimonial about a member getting a new client from committee involvement.
Desjoyaux Piscine Collegno è il Flagship Store Desjoyaux in Italia. Si trova sulla tangenziale di Torino a Collegno, vicino ai centri commerciali la Certosa, Ikea, Unieuro, Leroy Merlin, Kiabi, Burger King. email: info@desjoyaux.it tel: 0114010908
Integration of Other Software Components with the Agricultural Expert Systems...IJARTES
Expert System is a rapidly growing technology in
the field of Artificial Intelligence. It is a computer program
which captures the knowledge of a human expert on a given
problem, and uses this knowledge to solve problems in a
fashion similar to the expert. The system can assist the expert
during problem-solving, or act in the place of the expert in
those situations where the expertise is lacking. Expert systems
have been developed in such diverse areas as agriculture,
science, engineering, business, and medicine. In these areas,
they have increased the quality, efficiency, and competitive
leverage of the organizations employing the technology. This
paper highlights the major characteristics of expert systems,
reviews several systems developed for application in the area
of agriculture and an overview about the integration of other
software components with the agricultural expert systems.
Role of expert systems in agriculture and its
applications in efficient crop production and
protection technologies has been reviewed and
discussed in this paper. Different domains of
agriculture are highlighted where expert systems can
play an important role for an expert in transferring
expert-driven knowledge instantly at the level of
farmer’s field. This paper explores structure of an
expert system, role of expert system in agriculture
along with details of expert system developed in the
different field of agriculture and also possibilities of
designing, developing and implementation of an
expert system for agriculture would motivate
scientists and extension workers to investigate
possible applications for the benefit of entire
agricultural community.
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EARLY BLIGHT AND LATE BLIGHT DISEASE DETECTION ON POTATO LEAVES USING CONVOLU...IRJET Journal
This document proposes a method to detect early blight and late blight diseases on potato leaves using convolutional neural networks. It involves preprocessing leaf images, extracting features, and training a classifier to identify disease patterns and provide predictions. The proposed system is evaluated on performance metrics like accuracy, precision, and recall. Convolutional neural networks have achieved high accuracy in plant disease classification compared to traditional methods by automating feature extraction from images. This technique allows for early detection of diseases, helping farmers prevent crop damage and losses.
IRJET- Crop Prediction and Disease DetectionIRJET Journal
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The document discusses developing a real-time plant disease diagnosis mobile application. It aims to allow farmers to easily capture images of plant leaves using a mobile camera, send the images to a central system for analysis, and receive diagnoses and treatment recommendations. The proposed system would use image processing and data mining techniques to analyze leaf images for abnormalities, identify the plant species, recognize any diseases present, and recommend appropriate pesticides and estimate treatment costs. This would provide a low-cost, convenient solution for farmers to quickly diagnose and respond to plant diseases.
Leaf Disease Detection and Selection of Fertilizers using Artificial Neural N...IRJET Journal
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A Review: Plant leaf Disease Detection Using Convolution Neural Network in Ma...IRJET Journal
This document summarizes a research paper that reviews the use of convolutional neural networks (CNNs) for plant leaf disease detection. The research aims to classify images of tomato, potato, and pepper leaves to identify 15 common diseases with 97% accuracy. CNN algorithms are effective for image classification tasks like this one. The system allows farmers to easily upload leaf images and receive disease diagnoses and pesticide recommendations to prevent crop loss and increase yields. Deep learning approaches can help improve agricultural efficiency and productivity by automating disease identification.
A Review: Plant leaf Disease Detection Using Convolution Neural Network in Ma...IRJET Journal
This document summarizes a research paper that reviews the use of convolutional neural networks (CNNs) for plant leaf disease detection. The research aims to classify images of tomato, potato, and pepper leaves to identify 15 common diseases with 97% accuracy. CNN algorithms are effective for image classification tasks like this one. The system allows farmers to easily upload leaf images and receive disease diagnoses and pesticide recommendations to prevent crop loss and increase yields. Deep learning approaches can help improve agricultural efficiency and productivity by automating disease identification.
Fruit Disease Detection And Fertilizer RecommendationIRJET Journal
This document discusses a proposed system for fruit disease detection and fertilizer recommendation using image processing and convolutional neural networks (CNNs). It begins with an introduction to the importance of detecting fruit diseases early to prevent economic losses. It then reviews several existing related works that use techniques like CNNs, k-nearest neighbors, support vector machines, and image processing methods. The proposed system would capture images using a camera, preprocess the images, train a CNN model on a dataset of diseased and healthy fruit images to classify new images, and provide fertilizer/pesticide recommendations. The system is broken down into modules for the frontend user interface, data collection and preprocessing, model building using CNNs, and a backend for analysis and recommendations.
IRJET- Farmer Advisory: A Crop Disease Detection SystemIRJET Journal
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SOIL FERTILITY AND PLANT DISEASE ANALYZERIRJET Journal
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3) The system has the potential to revolutionize how farmers make decisions and help address challenges in Indian agriculture through timely recommendations accessible on mobile and web apps.
Using AI to Recommend Pesticides for Effective Management of Multiple Plant D...IRJET Journal
This document discusses using artificial intelligence to recommend pesticides for effective plant disease management. It presents a methodology using computer vision and machine learning, specifically convolutional neural networks (CNNs), to develop a system for detecting plant diseases. The system would analyze leaf images using CNNs and provide fertilizer recommendations to help farmers more easily and quickly identify diseases affecting their crops. This could help reduce excessive pesticide use and environmental damage while improving crop yields. The paper reviews several related works applying CNNs and other machine learning methods to identify diseases from images. It discusses acquiring and preprocessing leaf image datasets to train models for large-scale disease detection, which could support more sustainable and data-driven agricultural decision making.
IRJET- Survey of Crop Recommendation SystemsIRJET Journal
This document summarizes and compares several papers on crop recommendation systems. It discusses papers that use techniques like artificial neural networks, ensemble models combining multiple algorithms like random trees and KNN, and algorithms like SVM. The document also compares the modules used in different systems like location detection, data analysis, similarity detection and recommendation generation. It concludes that using ensemble methods can improve accuracy over single algorithms and future work could integrate more factors like economic conditions and land area into recommendation systems.
The document describes a smart farming app called Kissan Konnect that was developed to help farmers in India. The app provides several services including crop prediction based on soil and weather conditions, plant disease detection using image recognition, tool rental marketplace, weather updates, and climate information. It uses machine learning algorithms like random forest classifier to predict optimal crops and diagnose diseases by comparing uploaded photos to a database. The goal is to increase crop yields and reduce costs by helping farmers make better decisions around planting and disease management.
Deep learning for Precision farming: Detection of disease in plantsIRJET Journal
This document presents a method for detecting plant leaf diseases using deep learning and image processing techniques. The method uses the AlexNet convolutional neural network model to analyze images of leaves from a dataset. The images are preprocessed, augmented, and classified by AlexNet to identify different leaf diseases. A graphical user interface is also proposed to provide preventative measures for the detected leaf diseases. The study aims to help farmers identify diseases early to minimize crop loss and improve agricultural efficiency through automatic disease detection.
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Similar to Research Inventy : International Journal of Engineering and Science (20)
Cultivation Process Facilitator for Selected Five Crops in Dry Zone Sri Lanka
Research Inventy : International Journal of Engineering and Science
1. RESEARCH INVENTY: International Journal of Engineering and Science
ISBN: 2319-6483, ISSN: 2278-4721, Vol. 1, Issue 11
(December 2012), PP 34-39
www.researchinventy.com
Agro Genius: An Emergent Expert System for Querying
Agricultural Clarification Using Data Mining Technique
1
Dr. P. Kamalak Kannan, 2 K. Hemalatha
1
Asst. Professor, 2Research scholar
1, 2
Department of computer science Govt. Arts College (Autonomous) Salem-636007
Abstract: Data mining application in agriculture is relatively a new approach for predicting agricultural crop
productivity. This paper provides an expert system about agriculture which helps the farmer to cultivate the
crops for high yield and giving awareness about the organic farming. This expert system contains three sections
namely training, best combination for cultivation and awareness of organic farming. The training section gives
basic needs of agriculture. The second section is about predicting the best combination for high yield in the
crop cultivation. The third section gives awareness to farmers about organic farming. This system helps a new
farmer to query his clarifications related to agriculture for better yield before cultivation.
Keywords: Expert system – Data Mining – Classification – Decision Tree – Training – Testing.
I. Introduction
An expert system is a computer system that attempts to make a replica of specific human expert intelligent
activities. Typically, knowledge-based systems enable users with a problem to take counsel from a computer system
as they would an expert advisor to pinpoint what may be causing a problem and figure out how to solve a problem,
perform a task, or make a conclusion. Like a human expert, such a computer system can take out additional
information from a user by asking questions related to the problem through a consultation. It can also answer
questions asked by a user about why certain information is needed. It can make exhortation regarding the problem or
decision at the end of a consultation, and it can explain the reasoning steps gone through to reach its decision when
asked by a user. In Agriculture, there are many reasons accountable for low productivity. About one-third of land
holdings are very small less than one hectare in size. Due to small size of land holdings we cannot use latest way of
cultivation. Even today farmers are using very old methods, tools and enactment for farming. Farmers are not using
artificial ways of cultivation. Inputs like-better quality of seeds, fertilizers and pesticides are also not used by most
of the farmers. Utilization of marginal farmers is also responsible. There is also low productivity because of
increasing stress on land and the absence of bank credit. The Review papers describe about disease and solution for
particular crops. Hence an emergent expert system named Agro Genius has been designed and implemented in this
research for all types of end users in Agriculture.
II. Review Of Literature
A paper [1], an expert system exclusively for the integrated disease management in finger millet is being
presented by incorporating fuzzy logic method to frame the rules and apply defuzzification to attach a value to the
severity of the disease identified, based on which the control and remedial measures are suggested. Though there are
many methodologies available to identify the disease and evaluate the severity, based on which the
recommendations can be made, the most commonly used is the experience of the farmer and the knowledge of the
agriculturist. The expert system that is been developed is a blend of both the above mentioned factors along with the
application of technological advancements. Since the expert system has a module of acquiring new knowledge, the
new breed of diseases that attack the crop can also be recorded. The system thus developed can also be extended to
incorporate various other modules like integrated pest management, soil management and fertilizers management
making it a total solution provider for in all aspect and hence increasing the yield.
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2. Agro Genius: An Emergent Expert System for Querying Agricultural Clarification Using...
A paper [2] designed and implemented a corn disease remote diagnostic system, which is focused on the
prevention, diagnosis and control of diseases that affect China corn production. The knowledge acquisition process
was conducted based on the knowledge obtained from the literature and experts. Enhanced by more than 100 photos
and drawings that assist the use in the identification process, the corn disease remote diagnostic system is supported
by a data base containing information for the identification of 63 diseases. The system can be used as an
identification tool for farmers; this system has tried hard to unify the advanced Internet and information technology
and the researches of domestic domain experts, and provides one kind of highly effective corn disease diagnosis
method. By incorporating the value diagnosis to improve case retrieval, this system solved the problem that it is very
difficult to find match case in case retrieval.
III. The Proposed Methodology
The proposed Agro Genius system is an Expert System for agriculture. It is aimed at a collaborative
venture with eminent Agriculture Scientists and Experts with an magnificent team of computer Engineers,
programmers and designers. The program is divided into two aspects: Information System and Advisory System. In
Information system, the user can get all the static information about different organic cultivation and disadvantages
of inorganic cultivation. In Advisory System, the user will have an interaction with the expert system like a querying
system; the user has to answer the questions asked by the Expert System. Decision tree induction technique is
adopted in the present study to develop innovative approaches to predict the best combination for cultivating crops.
A decision tree is a flow-chart-like tree structure, where each internal node denotes a test on an attribute, each
branch typifies an outcome of the test, and leaf nodes typify classes or class distributions. The top most nodes in a
tree is the root node. In order to classify an unknown sample, the attribute values of the sample are tested against
the decision tree. A path is discovering from the root to a leaf node that holds the class prediction for that sample.
Decision trees were then coinciding to classification rules using IF-THEN-ELSE. Orthogonal Arrays (often referred
to Taguchi Methods) are often employed in industrial experiments to study the effect of several control factors. An
orthogonal array is a type of experiment where the columns for the independent variables are “orthogonal” to one
another. Benefits: Conclusions valid over the entire region spanned by the control factors and their settings, Large
saving in the experimental effort and Analysis is easy. To define an orthogonal array, one must identify: Number of
factors to be studied and Levels for each factor.
3.1 Rule Based System
In a Rule Based System the System takes Input and makes results with all the facts and Rules that matches
with in the Knowledge base. This Rule Based System contain of Knowledge Base, Inference Engine, User Interface,
Expert and the User. In the Rule Based System the systems acquire the inputs from the farmer or the user and give
the advice receptacle on the exact match of facts and rules from the knowledge base. The output of the this system
produce the classification based on the inputs provided by the user which most significant to a disadvantage that if
any of the inputs does not match with the knowledge it will not give any output for the further proceedings.
Fig 1.The Proposed System Architecture
This paper used two algorithms, namely Rule based Algorithm which is used to classify the users based on the
answer provided for expert systems questions.
35
3. Agro Genius: An Emergent Expert System for Querying Agricultural Clarification Using...
IV. Experimental Analysis
4.1 Dataset
The monitoring data is a basic agriculture detail with questions & answers, organic farming and good
combination to cultivate crop Advisory System the MYSQL database. It can be used as any other data stored in a
database. This greatly increases the opportunity with which you can conduct post-analysis of the monitoring data.
4.2 Rules
A set of rules, which constitute the program, stored in a rule memory of production memory and on an
inference engine required to execute the rules. Here the users are categories based on the marks scored for the
questions asked by the expert system. If the user score <= 4 the user will be moved to the training section. If the user
score <=7 the user will be moved to Advisory system. If the user answers all questions, the user will be moved to
awareness section.
4.2.1 Training section
It consists of basic needs of agriculture to train the user about fertilizer management, pest management,
farming system, weed controlling, soil types, water management and cropping seasons.
Table 1. Attributes and its Domain
ATTRIBUTE DOMAIN
A. Rain 1.High
2.Medium
3.Low
B. Soil 1.Red soil
2.Black Soil
3.Saline
C. Fertilizer management 1.Organic Fertilizer
2.Inorganic Fertilizer
3.Nill
D. Farming System 1.Mono crop
2.double crop
3.Mixed crop
E. Irrigation 1.1-15
2.1-30
3.1-60
F. Season 1.June-Aug
2.Oct-Dec
3.Feb-May
G. Weed controlling 1.Manual
2.Machine
3.Nill
H. Pest Management 1.Organic
2.Inorganic
3.Nill
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4. Agro Genius: An Emergent Expert System for Querying Agricultural Clarification Using...
Fig 2. Basic Information about Agriculture
4.2.2 Advisory section
This system is about finding a good combination to cultivate crop with the help of experts and the user
obtains suggestion about the combinations which will produce good yield and sowing is best or not. The basic
algorithm for decision tree induction is an insatiable algorithm that constructs decision trees in a top-down repetitive
divide-and-conquer manner.
Fig 3. Decision tree
The decision tree has number of combinations. It is difficult to analyze all the combinations and find out
the result. Hence the orthogonal technique is used select better combinations. The reduced combinations will be
consulted with the experts to find the possible combinations for high yield. The Expert System for identification of
combination of deficiency in plants has been based on inference and its theory base is in the order of first logic.
37
5. Agro Genius: An Emergent Expert System for Querying Agricultural Clarification Using...
4.2.3 Awareness section
In this section the users will be provided awareness about the usage of organic farming and also the
disadvantage of inorganic farming. The organic farming will reduce the global warming and also provides a full-
fledged eco-friendly agricultural system.
V. Result And Discussion
The knowledge base of this system contains production rules derived from the decision tree (figure-2).
L18(L8x3) Array
Conditional Attributes
Expt.
No. A B C D E F G H
1 1 1 1 1 1 1 1 1
2 1 1 2 2 2 2 2 2
3 1 1 3 3 3 3 3 3
4 1 2 1 1 2 2 3 3
5 1 2 2 2 3 3 1 1
6 1 2 3 3 1 1 2 2
7 1 3 1 2 1 3 2 3
8 1 3 2 3 2 1 3 1
9 1 3 3 1 3 2 1 2
10 2 1 1 3 3 2 2 1
11 2 1 2 1 1 3 3 2
12 2 1 3 2 2 1 1 3
13 2 2 1 2 3 1 3 2
14 2 2 2 3 1 2 1 3
15 2 2 3 1 2 3 2 1
16 2 3 1 3 2 3 1 2
17 2 3 2 1 3 1 2 3
18 2 3 3 2 1 2 3 1
Table 2. Number of Combination
The best combination will be selected and implemented for high productivity.
Fig 4. Best combination for maize
38
6. Agro Genius: An Emergent Expert System for Querying Agricultural Clarification Using...
Fig 5. Awareness about Organic Farming
VI. Research Directions
This kind of research will be useful for all users. Farmer can easily identify the ideal crop for particular
monsoon season. In future it can be further developed to be approachability by the mobile phones. The mobile
platform provides the advantage for person to obtain consultation practically anytime and anywhere. Animation,
sound and video features can be added to behave like Guru because Guru does not issue only facts, but explains and
provides solutions in the form of examples. It can also work as a Guru.
VII. Conclusion
This paper provides an Expert system for agriculture. This expert system provides basic information about
agriculture for the beginners in farming, giving the best combination for cultivate the crops and creating awareness
about the organic farms .This expert system advices and suggestions in the area of crop field by providing facilities
like dynamic interaction between expert system and the user without the need of expert (crop) at all times. By the
interaction with the users and beneficiaries the functionality of the System can be extended further to many more
areas in and around the world. A proper understanding of the above section will result in a better agriculture system.
Reference
[1] PhilomineRoseline,”Design and Development of Fuzzy Expert System for Integrated Disease Management in Finger Millets”
International Journal of Computer Applications (0975 – 8887) Volume 56– No.1, October 2012
[2] Xinxing Li, Lingxian Zhang,” The corn disease remote diagnostic system in China” Journal of Food, Agriculture & Environment Vol.10
(1): 617-620. 2012.
[3]. Prof. Chandrakanth. Biradar,” An Statistical Based Agriculture Data Analysis” International Journal of Emerging Technology and
Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 9, September 2012).
[4] S.S.Patil, B.V.Dhandra, “Web based Expert System for Diagnosis of Micro Nutrients’ Deficiencies in Crops” proceedings of the World
Congress on Engineering and Computer Science 2009 Vol I WCECS 2009, October 20-22, 2009, San Francisco, USA
[5]. S.Veenadhari, Dr.BharatMishra,”Soybean Productivity Modelling using Decision Tree Algorithms“ International Journal of Computer
Applications (0975 – 8887) Volume 27– No.7, August 2011
[6]. Kym Anderson and Anna Strutt Agriculture and Food Security in Asia by 2030
[7]. Puja Shrivastava,” Implementation of an Expert System as Spiritual Guru for Personality Development” International Journal of
Computer Theory and Engineering, Vol.3, No.1, February, 2011,1793-8201
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