Accurate prediction of crop yield is very difficult.because it depends on nature which is chane day by day. The historical data is insufficient and inevident due to the naturo climatical changes . The poverty of people's thirst still unbalanced. To avoid the drawback of existing manual prediction, use deep learning methods to predict the future crops more accurately during this uneven climate. The soil, weather and climate features are taken as a parameters. In past few years machine learning algorithms used to predict the future forecast. Here deep neural network is introduced to get more accurate data than other networks. The online gradient descent used to find the online learning deep neural network depth. It automatically decides the depth of the neural network
Crop yield prediction using deep learning.pdfssuserb22f5a
Reliable prediction of crop yield is important in the creation of successful regional and global agricultural and food policies. When production has been predicted, farm inputs like fertilizers will vary according to plant and soil requirements. Different techniques have been proposed for accurate prediction of crop yield. Deep Neural Network (DNN) was introduced to understand the environmental factor i.e., weather data for accurate yield prediction. In order to enhance the accuracy of yield prediction, Multi-Model Ensemble with DNN (MMEDNN) is introduced where climate, weather and soil data are considered for yield prediction. A statistical model is used to find the variation of climate, weather and soil parameters from year-to-year. These variations are used for climate, weather and soil predictions and these predictions play an important role in yield prediction. The predicted climate, weather and soil parameters are given as input to DNN for yield prediction. The yield prediction accuracy is improved by considering various environmental data.
Crop yield prediction using data mining techniques.pdfssuserb22f5a
Agriculture is the main source of occupation which forms the backbone of our country. It involves the production of crops which may be either food crops or commercial crops. The productivity of crop yield is significantly influenced by various parameters such as rainfall, farm capacity, temperature, crop population density, humidity, irrigation, fertilizer application, solar radiation, type of soil, depth, tillage and soil organic matter. An accurate crop yield prediction support decision-makers in the agriculture sector to predict the yield effectively. Machine learning techniques and deep learning techniques play a significant role in the analysis of data for crop yield prediction. However, the selection of appropriate techniques from the pool of available techniques imposes challenges to the researchers concerning the chosen crop. In this paper, an analysis has been performed on various deep learning and machine learning techniques. To know the limitations of each technique, a comparative analysis is carried out in this paper. In addition to this, a suggestion is provided to further improve the performance of crop yield prediction.
Crop yield prediction using ridge regression.pdfssuserb22f5a
Crop yield prediction using deep neural networks with data mining concepts by applying multi model ensembles using ridge regression to increase accuracy, precision, recall,and f measure. Combining neural networks with regression increase high satisfactory crop yield prediction.the support vector regression is slow convergence , stuck in local minima. But ridge regression analyse multicollinearity in multiple regression.
This document summarizes different analytical models that can be used for crop prediction, including classification models. It discusses feature selection methods like principal component analysis and information gain that are important for crop prediction models. The document reviews different machine learning techniques used in previous studies for crop yield prediction, such as linear regression, k-nearest neighbors, neural networks, support vector machines, and decision trees. It aims to compare the performance of these classification techniques for predicting crop yields based on parameters like temperature, rainfall, soil characteristics, and more.
IRJET- Rice Yield Prediction using Data Mining TechniqueIRJET Journal
This document discusses using data mining techniques to predict rice yield based on soil parameters. It proposes using a K-Nearest Neighbors (KNN) algorithm to analyze past agricultural data like temperature, rainfall, humidity and soil properties to determine the rice yield. The KNN algorithm calculates Euclidean distances between input parameters and existing data to find the K closest matches and predict output based on similarity measures. The approach aims to help farmers increase yields and avoid losses by providing accurate rice yield predictions based on different parameters. It concludes that soil properties and weather significantly impact rice yields and the proposed data mining method can predict yields but may not be fully accurate if weather changes.
Agriculture crop yield prediction using inertia based cat swarm optimizationIJECEIAES
Crop yield prediction is among the most important and main sources of income in the Indian economy. In this paper, the improved cat swarm optimization (ICSO) based recurrent neural network (RNN) model is proposed for crop yield prediction using time series data. The inertia weight parameter is added to position equation that is selected randomly, and a new velocity equation is produced which enhances the searching ability in the best cat area. By using inertia weight, the ICSO enhances performance of feature selection and obtains better convergence in minimum iteration. The RNN is applied to produce direct graph using sequence of data and decides current layer output by involving all other existing calculations. The performance of the model is estimated using coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE) on the yield from the years 2011 to 2021 with an annual prediction for 120 records of approximately 8 million nuts. The evaluated result shows that the proposed ICSO-RNN model delivers metrics such as R2, MAE, MSE, and RMSE values of 0.99, 0.77, 0.68, and 0.82 correspondingly, which ensures accurate yield prediction when compared with the existing methods which are hybrid reinforcement learning-random forest (RL-RF) and machine learning (ML) methods.
Supervise Machine Learning Approach for Crop Yield Prediction in Agriculture ...IRJET Journal
This document summarizes a research paper that proposes using machine learning techniques like linear regression to predict crop yields in the agricultural sector. Specifically, it aims to build models using past agricultural data to help farmers predict yields before planting and determine how to allocate resources for maximum profit. The proposed system would analyze factors like environment, soil, water, temperature and use ML algorithms to forecast yields and provide smart recommendations to farmers. It discusses previous related work on crop yield prediction using machine learning and the limitations of existing systems. The goal is to develop an accurate predictive model using a supervised learning approach that farmers can use to make informed financial decisions.
IRJET- Agricultural Crop Yield Prediction using Deep Learning ApproachIRJET Journal
This document discusses using artificial neural networks to predict agricultural crop yields. It begins with an abstract that outlines using ANNs to predict crop yield given various input parameters like soil pH, nitrogen levels, temperature, rainfall, etc. It then provides an introduction on the importance of accurate crop yield prediction. The next sections discuss literature on previous ANN crop yield prediction models, the proposed ANN approach including network architecture and activation functions, the design process, and conclusions. The key points are that ANNs can accurately predict crop yields given various climatic and soil inputs, and providing farmers with these predictions could help maximize profits and minimize losses.
Crop yield prediction using deep learning.pdfssuserb22f5a
Reliable prediction of crop yield is important in the creation of successful regional and global agricultural and food policies. When production has been predicted, farm inputs like fertilizers will vary according to plant and soil requirements. Different techniques have been proposed for accurate prediction of crop yield. Deep Neural Network (DNN) was introduced to understand the environmental factor i.e., weather data for accurate yield prediction. In order to enhance the accuracy of yield prediction, Multi-Model Ensemble with DNN (MMEDNN) is introduced where climate, weather and soil data are considered for yield prediction. A statistical model is used to find the variation of climate, weather and soil parameters from year-to-year. These variations are used for climate, weather and soil predictions and these predictions play an important role in yield prediction. The predicted climate, weather and soil parameters are given as input to DNN for yield prediction. The yield prediction accuracy is improved by considering various environmental data.
Crop yield prediction using data mining techniques.pdfssuserb22f5a
Agriculture is the main source of occupation which forms the backbone of our country. It involves the production of crops which may be either food crops or commercial crops. The productivity of crop yield is significantly influenced by various parameters such as rainfall, farm capacity, temperature, crop population density, humidity, irrigation, fertilizer application, solar radiation, type of soil, depth, tillage and soil organic matter. An accurate crop yield prediction support decision-makers in the agriculture sector to predict the yield effectively. Machine learning techniques and deep learning techniques play a significant role in the analysis of data for crop yield prediction. However, the selection of appropriate techniques from the pool of available techniques imposes challenges to the researchers concerning the chosen crop. In this paper, an analysis has been performed on various deep learning and machine learning techniques. To know the limitations of each technique, a comparative analysis is carried out in this paper. In addition to this, a suggestion is provided to further improve the performance of crop yield prediction.
Crop yield prediction using ridge regression.pdfssuserb22f5a
Crop yield prediction using deep neural networks with data mining concepts by applying multi model ensembles using ridge regression to increase accuracy, precision, recall,and f measure. Combining neural networks with regression increase high satisfactory crop yield prediction.the support vector regression is slow convergence , stuck in local minima. But ridge regression analyse multicollinearity in multiple regression.
This document summarizes different analytical models that can be used for crop prediction, including classification models. It discusses feature selection methods like principal component analysis and information gain that are important for crop prediction models. The document reviews different machine learning techniques used in previous studies for crop yield prediction, such as linear regression, k-nearest neighbors, neural networks, support vector machines, and decision trees. It aims to compare the performance of these classification techniques for predicting crop yields based on parameters like temperature, rainfall, soil characteristics, and more.
IRJET- Rice Yield Prediction using Data Mining TechniqueIRJET Journal
This document discusses using data mining techniques to predict rice yield based on soil parameters. It proposes using a K-Nearest Neighbors (KNN) algorithm to analyze past agricultural data like temperature, rainfall, humidity and soil properties to determine the rice yield. The KNN algorithm calculates Euclidean distances between input parameters and existing data to find the K closest matches and predict output based on similarity measures. The approach aims to help farmers increase yields and avoid losses by providing accurate rice yield predictions based on different parameters. It concludes that soil properties and weather significantly impact rice yields and the proposed data mining method can predict yields but may not be fully accurate if weather changes.
Agriculture crop yield prediction using inertia based cat swarm optimizationIJECEIAES
Crop yield prediction is among the most important and main sources of income in the Indian economy. In this paper, the improved cat swarm optimization (ICSO) based recurrent neural network (RNN) model is proposed for crop yield prediction using time series data. The inertia weight parameter is added to position equation that is selected randomly, and a new velocity equation is produced which enhances the searching ability in the best cat area. By using inertia weight, the ICSO enhances performance of feature selection and obtains better convergence in minimum iteration. The RNN is applied to produce direct graph using sequence of data and decides current layer output by involving all other existing calculations. The performance of the model is estimated using coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE) on the yield from the years 2011 to 2021 with an annual prediction for 120 records of approximately 8 million nuts. The evaluated result shows that the proposed ICSO-RNN model delivers metrics such as R2, MAE, MSE, and RMSE values of 0.99, 0.77, 0.68, and 0.82 correspondingly, which ensures accurate yield prediction when compared with the existing methods which are hybrid reinforcement learning-random forest (RL-RF) and machine learning (ML) methods.
Supervise Machine Learning Approach for Crop Yield Prediction in Agriculture ...IRJET Journal
This document summarizes a research paper that proposes using machine learning techniques like linear regression to predict crop yields in the agricultural sector. Specifically, it aims to build models using past agricultural data to help farmers predict yields before planting and determine how to allocate resources for maximum profit. The proposed system would analyze factors like environment, soil, water, temperature and use ML algorithms to forecast yields and provide smart recommendations to farmers. It discusses previous related work on crop yield prediction using machine learning and the limitations of existing systems. The goal is to develop an accurate predictive model using a supervised learning approach that farmers can use to make informed financial decisions.
IRJET- Agricultural Crop Yield Prediction using Deep Learning ApproachIRJET Journal
This document discusses using artificial neural networks to predict agricultural crop yields. It begins with an abstract that outlines using ANNs to predict crop yield given various input parameters like soil pH, nitrogen levels, temperature, rainfall, etc. It then provides an introduction on the importance of accurate crop yield prediction. The next sections discuss literature on previous ANN crop yield prediction models, the proposed ANN approach including network architecture and activation functions, the design process, and conclusions. The key points are that ANNs can accurately predict crop yields given various climatic and soil inputs, and providing farmers with these predictions could help maximize profits and minimize losses.
IMPLEMENTATION PAPER ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document presents an implementation paper on an agriculture advisory system that uses machine learning algorithms to predict optimal crops and recommend fertilizers. It first reviews previous literature on similar crop prediction systems using data mining techniques. It then describes the proposed system's methodology, which involves 8 steps: data collection, preprocessing, training, supervised learning using Naive Bayes for crop prediction and KNN for fertilizer recommendation, priority-based crop recommendation, location- and year-based recommendations, output of results, and visual representation of recommendations. The system aims to help farmers select profitable crops and increase agricultural output by providing customized recommendations based on soil analysis and other input data. It concludes the proposed system could help address issues farmers face by streamlining information and facilitating efficient
CROP YIELD PREDICTION USING ARTIFICIAL NEURAL NETWORKIRJET Journal
1) The document proposes predicting crop yields using an artificial neural network (ANN) approach. Seven years of data on factors like rainfall, temperature, and soil type were collected from an agricultural region in India.
2) An ANN with forward and backward propagation is implemented and weights/biases are determined from these processes. The predicted yields from the ANN are compared to other models like support vector machines to evaluate performance.
3) Preliminary results show the ANN approach can predict crop yields with higher accuracy than other methods. Further expanding the dataset and crops could increase prediction accuracy even more for better agricultural planning and productivity.
A COMPREHENSIVE SURVEY ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document provides a literature review and proposed methodology for an agricultural advisory system using data science techniques. It discusses several past studies that used machine learning algorithms like Naive Bayes, KNN, decision trees, and clustering for crop prediction and recommendations. The proposed system would collect agricultural data on parameters like rainfall, temperature and soil composition. It would preprocess, train and apply a supervised learning algorithm like Naive Bayes to provide priority-based crop recommendations to farmers based on location and year. The goal is to help farmers select suitable high-profit crops using data-driven techniques.
Agricultural Product Price and Crop Cultivation Prediction based on Data Scie...IRJET Journal
This document summarizes a research paper that examines using machine learning techniques to predict agricultural crop prices and crop cultivation based on available data. It discusses using supervised machine learning classification algorithms like decision trees, random forests, naive bayes, and support vector machines to analyze crop dataset variables and predict crop yields. The goal is to develop an accurate machine learning model for crop prediction to help farmers and the agricultural economy by replacing updatable supervised models and predicting results with the best accuracy by comparing algorithms.
Analysis of crop yield prediction using data mining techniqueseSAT Journals
Abstract
Agrarian sector in India is facing rigorous problem to maximize the crop productivity. More than 60 percent of the crop still depends on monsoon rainfall. Recent developments in Information Technology for agriculture field has become an interesting research area to predict the crop yield. The problem of yield prediction is a major problem that remains to be solved based on available data. Data Mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. This paper presents a brief analysis of crop yield prediction using Multiple Linear Regression (MLR) technique and Density based clustering technique for the selected region i.e. East Godavari district of Andhra Pradesh in India.
Keywords: Agrarian Sector, Crop Production, Data Mining, Density based clustering, Information Technology, Multiple Linear Regression, Yield Prediction.
IRJET - Analysis of Crop Yield Prediction by using Machine Learning AlgorithmsIRJET Journal
This document analyzes crop yield prediction using machine learning algorithms like K-Nearest Neighbor and Support Vector Machine. It discusses collecting agricultural data from various regions on factors like rainfall, humidity, temperature, area, yield, soil type and location. The data is preprocessed, transformed and split into training and testing sets. Both KNN and SVM are applied to the data and SVM is found to have higher accuracy and faster execution time compared to KNN in predicting suitable crops and estimated yields. The proposed system provides farmers an efficient way to predict crops and yields for their region using modern machine learning techniques.
IRJET - Agricultural Analysis using Data Mining TechniquesIRJET Journal
This document discusses using data mining techniques like K-means clustering, KNN classification, Bayesian networks, and support vector machines to analyze agricultural data and predict crop production. It analyzes data related to rainfall, temperature, area planted, and production for various crops. These techniques are applied to the agricultural data set to accurately predict future crop yields. Data mining plays an important role in solving problems in agriculture by analyzing large datasets and identifying patterns.
Statistical features learning to predict the crop yield in regional areasIJECEIAES
The plethora of information presented in the form of benchmark dataset plays a significant role in analyzing and understanding the crop yield in certain regions of regional territory. The information may be presented in the form of attributes makes a prediction of crop yield in various regions of machine learning. The information considered for processing involves data cleaning initially followed by binning to reduce the missing data. The information collected is subjected to clustering of data items based on patterns of similarity, The data items that are similar in nature is fed to the system with similarity measure, which involves understanding the distance of data items from its related data item leading to hyper parameters for analyzing of information while calculating the crop yield. The information may be used to ascertain the patterns of data that exhibit similarity with nearest neighbor represented by another attribute. Thus, the research method has yielded an accuracy of 89.62% of classification for predicting the crop yield in agricultural areas of Karnataka region.
Netica is a Bayesian network modeling and inference software package developed by Norsys Software Corp. It allows users to build and evaluate causal probabilistic models known as Bayesian networks.
R: R is a programming language and software environment for statistical analysis, graphics, and statistical computing. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues.
Weka: Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules
IRJET - Intelligent Weather Forecasting using Machine Learning TechniquesIRJET Journal
This document discusses using machine learning techniques to forecast weather intelligently. It proposes using multi-target regression and recurrent neural network (RNN) models trained on historical weather data from Bangalore to predict future weather conditions like temperature, humidity, and precipitation. The data is first preprocessed before being fed to the models. The models are evaluated to accurately predict weather in the short term to help people like farmers and commuters without relying on expensive equipment.
IRJET- Agricultural Data Modeling and Yield Forecasting using Data Mining...IRJET Journal
This document proposes using data mining techniques to develop a predictive model for forecasting crop yields. It involves collecting agricultural data on factors like rainfall, temperature, seed quality, and sowing procedures. Data preprocessing and clustering techniques like K-means are applied. Classification algorithms like Support Vector Machine and Naive Bayes are used to predict crop yield as low, medium, or high. The predictive model aims to help farmers plan cultivation for high crop yields by identifying the best combinations of agricultural factors.
This document discusses applications of data mining techniques in agriculture. It begins by explaining how data mining can extract useful patterns from large datasets and how fuzzy sets are well-suited for handling incomplete agricultural data involving human factors. Next, it summarizes several common data mining techniques like clustering, association rules, functional dependencies, and data summarization and provides examples of each applied to agriculture, such as for weather forecasting, soil classification, and yield prediction. The document concludes by comparing statistical analysis to data mining and finding data mining superior for analyzing agricultural datasets for speed, ease of use, and accuracy.
Smart Farming Using Machine Learning AlgorithmsIRJET Journal
This document discusses using machine learning algorithms for smart farming and crop prediction. It proposes a system that collects sensor data on temperature, soil moisture, and other variables to predict optimal crops for a farm. An artificial neural network (ANN) algorithm is identified as suitable for this application due to its ability to learn from large datasets like those collected from IoT sensors. The system architecture involves sensors collecting data which is sent to a Raspberry Pi for processing and training a predictive model. This model would then provide crop recommendations to farmers to help guide decisions and reduce risks from climate change.
1. The document discusses the development of a machine learning-based system to provide precise crop yield recommendations to farmers in India.
2. Over 60% of Indians work in agriculture but farmers often grow the same crops without trying new varieties and apply fertilizers inconsistently, affecting yields and soil quality.
3. The proposed system aims to address these issues by recommending the optimal crop for a given plot of land based on soil composition and environmental factors using machine learning algorithms.
A Novel Deep Learning-Based Hybrid Method for the Determination of Productivi...Shakas Technologies
A Novel Deep Learning-Based Hybrid Method for the Determination of Productivity of Agricultural Products Apple Case Study.
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website: www.shakastech.com
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Development of Effective Crop Monitoring and Management System with Weather R...IRJET Journal
This document describes a proposed crop monitoring and management system for hill station farmers that uses machine learning algorithms and wireless communication. Sensors in farmlands would transmit real-time data via LoRa to a local processing station. The system would use KNN, random forest, and LSTM algorithms to provide crop recommendations, weather predictions, and agri-ratings to help farmers remotely monitor and manage their crops. Initial results found the KNN algorithm provided 97% accuracy for crop recommendations, while random forest and LSTM achieved 85% and 81% accuracy respectively for weather predictions and agri-ratings. The system aims to help hill station farmers who face challenges in remotely accessing farmlands.
IRJET - Agrotech: Soil Analysis and Crop PredictionIRJET Journal
This document presents a system for soil analysis and crop prediction using data mining techniques. The system measures soil parameters like pH, nitrogen, phosphorus and potassium using sensors. It then uses a decision tree algorithm to classify the soil and predict suitable crops. The pH value is used to estimate other nutrient values. The nutrient values and soil type are sent over WiFi to a server, which uses machine learning to predict crops and provide fertilizer recommendations to the farmer. The proposed system automates the soil testing process and aims to help farmers select optimal crops and increase agricultural yields.
Selection of crop varieties and yield prediction based on phenotype applying ...IJECEIAES
In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping season, and crop region. The key objective is to predict the suitable crop for the farmers based on their locations, soil types, and environmental factors. This results in less financial loss and a shorter crop production timeframe. Combined feature selection (CFS)-based machine regression helps increase crop production rates. A brief comparative analysis was also made between various machine learning (ML) regression algorithms, which majorly contributed to the process of crop selection considering phenotype factors. Stacked long short-term memory (LSTM) classifiers outperformed other decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR) with a prediction accuracy of 93% with the lowest classification accuracy metrics. The proposed method can help us select the perfect crop for maximum yield.
IRJET - Enlightening Farmers on Crop YieldIRJET Journal
This document discusses using data mining techniques to predict crop yields to help farmers. It proposes using a random forest regression algorithm on past agricultural data from 2000-2014 to build a prediction model. The model would help farmers select optimal crops, understand weather patterns, and maximize yields. The system is described as gathering data, preprocessing it, training a random forest model on 60% of the data and testing it on 20%. It would then provide yield predictions and recommendations to farmers through a visualization tool. The goal is to help guide farmers' decisions around fertilizer use, soil management, and crop selection to improve production levels.
AI Based Smart Agriculture – Leaf Disease Prediction Using Optimized CNN ModelIRJET Journal
This document discusses using optimized convolutional neural network (CNN) models for leaf disease prediction in smart agriculture. Sensors are used to collect environmental data from fields, and images of plant leaves are analyzed for disease identification. Three CNN methods - fast R-CNN, faster R-CNN, and Mask R-CNN - are evaluated and the best method is selected based on prediction accuracy. The optimized CNN model identifies diseases and recommends suitable pesticides, while sensor data is also used for irrigation control and crop recommendations based on soil conditions. The system aims to help farmers detect diseases early and improve crop productivity using artificial intelligence and internet of things technologies.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
IMPLEMENTATION PAPER ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document presents an implementation paper on an agriculture advisory system that uses machine learning algorithms to predict optimal crops and recommend fertilizers. It first reviews previous literature on similar crop prediction systems using data mining techniques. It then describes the proposed system's methodology, which involves 8 steps: data collection, preprocessing, training, supervised learning using Naive Bayes for crop prediction and KNN for fertilizer recommendation, priority-based crop recommendation, location- and year-based recommendations, output of results, and visual representation of recommendations. The system aims to help farmers select profitable crops and increase agricultural output by providing customized recommendations based on soil analysis and other input data. It concludes the proposed system could help address issues farmers face by streamlining information and facilitating efficient
CROP YIELD PREDICTION USING ARTIFICIAL NEURAL NETWORKIRJET Journal
1) The document proposes predicting crop yields using an artificial neural network (ANN) approach. Seven years of data on factors like rainfall, temperature, and soil type were collected from an agricultural region in India.
2) An ANN with forward and backward propagation is implemented and weights/biases are determined from these processes. The predicted yields from the ANN are compared to other models like support vector machines to evaluate performance.
3) Preliminary results show the ANN approach can predict crop yields with higher accuracy than other methods. Further expanding the dataset and crops could increase prediction accuracy even more for better agricultural planning and productivity.
A COMPREHENSIVE SURVEY ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document provides a literature review and proposed methodology for an agricultural advisory system using data science techniques. It discusses several past studies that used machine learning algorithms like Naive Bayes, KNN, decision trees, and clustering for crop prediction and recommendations. The proposed system would collect agricultural data on parameters like rainfall, temperature and soil composition. It would preprocess, train and apply a supervised learning algorithm like Naive Bayes to provide priority-based crop recommendations to farmers based on location and year. The goal is to help farmers select suitable high-profit crops using data-driven techniques.
Agricultural Product Price and Crop Cultivation Prediction based on Data Scie...IRJET Journal
This document summarizes a research paper that examines using machine learning techniques to predict agricultural crop prices and crop cultivation based on available data. It discusses using supervised machine learning classification algorithms like decision trees, random forests, naive bayes, and support vector machines to analyze crop dataset variables and predict crop yields. The goal is to develop an accurate machine learning model for crop prediction to help farmers and the agricultural economy by replacing updatable supervised models and predicting results with the best accuracy by comparing algorithms.
Analysis of crop yield prediction using data mining techniqueseSAT Journals
Abstract
Agrarian sector in India is facing rigorous problem to maximize the crop productivity. More than 60 percent of the crop still depends on monsoon rainfall. Recent developments in Information Technology for agriculture field has become an interesting research area to predict the crop yield. The problem of yield prediction is a major problem that remains to be solved based on available data. Data Mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. This paper presents a brief analysis of crop yield prediction using Multiple Linear Regression (MLR) technique and Density based clustering technique for the selected region i.e. East Godavari district of Andhra Pradesh in India.
Keywords: Agrarian Sector, Crop Production, Data Mining, Density based clustering, Information Technology, Multiple Linear Regression, Yield Prediction.
IRJET - Analysis of Crop Yield Prediction by using Machine Learning AlgorithmsIRJET Journal
This document analyzes crop yield prediction using machine learning algorithms like K-Nearest Neighbor and Support Vector Machine. It discusses collecting agricultural data from various regions on factors like rainfall, humidity, temperature, area, yield, soil type and location. The data is preprocessed, transformed and split into training and testing sets. Both KNN and SVM are applied to the data and SVM is found to have higher accuracy and faster execution time compared to KNN in predicting suitable crops and estimated yields. The proposed system provides farmers an efficient way to predict crops and yields for their region using modern machine learning techniques.
IRJET - Agricultural Analysis using Data Mining TechniquesIRJET Journal
This document discusses using data mining techniques like K-means clustering, KNN classification, Bayesian networks, and support vector machines to analyze agricultural data and predict crop production. It analyzes data related to rainfall, temperature, area planted, and production for various crops. These techniques are applied to the agricultural data set to accurately predict future crop yields. Data mining plays an important role in solving problems in agriculture by analyzing large datasets and identifying patterns.
Statistical features learning to predict the crop yield in regional areasIJECEIAES
The plethora of information presented in the form of benchmark dataset plays a significant role in analyzing and understanding the crop yield in certain regions of regional territory. The information may be presented in the form of attributes makes a prediction of crop yield in various regions of machine learning. The information considered for processing involves data cleaning initially followed by binning to reduce the missing data. The information collected is subjected to clustering of data items based on patterns of similarity, The data items that are similar in nature is fed to the system with similarity measure, which involves understanding the distance of data items from its related data item leading to hyper parameters for analyzing of information while calculating the crop yield. The information may be used to ascertain the patterns of data that exhibit similarity with nearest neighbor represented by another attribute. Thus, the research method has yielded an accuracy of 89.62% of classification for predicting the crop yield in agricultural areas of Karnataka region.
Netica is a Bayesian network modeling and inference software package developed by Norsys Software Corp. It allows users to build and evaluate causal probabilistic models known as Bayesian networks.
R: R is a programming language and software environment for statistical analysis, graphics, and statistical computing. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues.
Weka: Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules
IRJET - Intelligent Weather Forecasting using Machine Learning TechniquesIRJET Journal
This document discusses using machine learning techniques to forecast weather intelligently. It proposes using multi-target regression and recurrent neural network (RNN) models trained on historical weather data from Bangalore to predict future weather conditions like temperature, humidity, and precipitation. The data is first preprocessed before being fed to the models. The models are evaluated to accurately predict weather in the short term to help people like farmers and commuters without relying on expensive equipment.
IRJET- Agricultural Data Modeling and Yield Forecasting using Data Mining...IRJET Journal
This document proposes using data mining techniques to develop a predictive model for forecasting crop yields. It involves collecting agricultural data on factors like rainfall, temperature, seed quality, and sowing procedures. Data preprocessing and clustering techniques like K-means are applied. Classification algorithms like Support Vector Machine and Naive Bayes are used to predict crop yield as low, medium, or high. The predictive model aims to help farmers plan cultivation for high crop yields by identifying the best combinations of agricultural factors.
This document discusses applications of data mining techniques in agriculture. It begins by explaining how data mining can extract useful patterns from large datasets and how fuzzy sets are well-suited for handling incomplete agricultural data involving human factors. Next, it summarizes several common data mining techniques like clustering, association rules, functional dependencies, and data summarization and provides examples of each applied to agriculture, such as for weather forecasting, soil classification, and yield prediction. The document concludes by comparing statistical analysis to data mining and finding data mining superior for analyzing agricultural datasets for speed, ease of use, and accuracy.
Smart Farming Using Machine Learning AlgorithmsIRJET Journal
This document discusses using machine learning algorithms for smart farming and crop prediction. It proposes a system that collects sensor data on temperature, soil moisture, and other variables to predict optimal crops for a farm. An artificial neural network (ANN) algorithm is identified as suitable for this application due to its ability to learn from large datasets like those collected from IoT sensors. The system architecture involves sensors collecting data which is sent to a Raspberry Pi for processing and training a predictive model. This model would then provide crop recommendations to farmers to help guide decisions and reduce risks from climate change.
1. The document discusses the development of a machine learning-based system to provide precise crop yield recommendations to farmers in India.
2. Over 60% of Indians work in agriculture but farmers often grow the same crops without trying new varieties and apply fertilizers inconsistently, affecting yields and soil quality.
3. The proposed system aims to address these issues by recommending the optimal crop for a given plot of land based on soil composition and environmental factors using machine learning algorithms.
A Novel Deep Learning-Based Hybrid Method for the Determination of Productivi...Shakas Technologies
A Novel Deep Learning-Based Hybrid Method for the Determination of Productivity of Agricultural Products Apple Case Study.
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Development of Effective Crop Monitoring and Management System with Weather R...IRJET Journal
This document describes a proposed crop monitoring and management system for hill station farmers that uses machine learning algorithms and wireless communication. Sensors in farmlands would transmit real-time data via LoRa to a local processing station. The system would use KNN, random forest, and LSTM algorithms to provide crop recommendations, weather predictions, and agri-ratings to help farmers remotely monitor and manage their crops. Initial results found the KNN algorithm provided 97% accuracy for crop recommendations, while random forest and LSTM achieved 85% and 81% accuracy respectively for weather predictions and agri-ratings. The system aims to help hill station farmers who face challenges in remotely accessing farmlands.
IRJET - Agrotech: Soil Analysis and Crop PredictionIRJET Journal
This document presents a system for soil analysis and crop prediction using data mining techniques. The system measures soil parameters like pH, nitrogen, phosphorus and potassium using sensors. It then uses a decision tree algorithm to classify the soil and predict suitable crops. The pH value is used to estimate other nutrient values. The nutrient values and soil type are sent over WiFi to a server, which uses machine learning to predict crops and provide fertilizer recommendations to the farmer. The proposed system automates the soil testing process and aims to help farmers select optimal crops and increase agricultural yields.
Selection of crop varieties and yield prediction based on phenotype applying ...IJECEIAES
In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping season, and crop region. The key objective is to predict the suitable crop for the farmers based on their locations, soil types, and environmental factors. This results in less financial loss and a shorter crop production timeframe. Combined feature selection (CFS)-based machine regression helps increase crop production rates. A brief comparative analysis was also made between various machine learning (ML) regression algorithms, which majorly contributed to the process of crop selection considering phenotype factors. Stacked long short-term memory (LSTM) classifiers outperformed other decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR) with a prediction accuracy of 93% with the lowest classification accuracy metrics. The proposed method can help us select the perfect crop for maximum yield.
IRJET - Enlightening Farmers on Crop YieldIRJET Journal
This document discusses using data mining techniques to predict crop yields to help farmers. It proposes using a random forest regression algorithm on past agricultural data from 2000-2014 to build a prediction model. The model would help farmers select optimal crops, understand weather patterns, and maximize yields. The system is described as gathering data, preprocessing it, training a random forest model on 60% of the data and testing it on 20%. It would then provide yield predictions and recommendations to farmers through a visualization tool. The goal is to help guide farmers' decisions around fertilizer use, soil management, and crop selection to improve production levels.
AI Based Smart Agriculture – Leaf Disease Prediction Using Optimized CNN ModelIRJET Journal
This document discusses using optimized convolutional neural network (CNN) models for leaf disease prediction in smart agriculture. Sensors are used to collect environmental data from fields, and images of plant leaves are analyzed for disease identification. Three CNN methods - fast R-CNN, faster R-CNN, and Mask R-CNN - are evaluated and the best method is selected based on prediction accuracy. The optimized CNN model identifies diseases and recommends suitable pesticides, while sensor data is also used for irrigation control and crop recommendations based on soil conditions. The system aims to help farmers detect diseases early and improve crop productivity using artificial intelligence and internet of things technologies.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers