The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine l...The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine learning(ML)models effectively deal with such challenges.This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024.In addition,it analyses the effectiveness of various input parameters considered in crop yield prediction models.We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield.The total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is 125.We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers.Each study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel performance.We also discuss the ethical and social impacts of AI on agriculture.However,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven models.Therefore,thorough research is required to deal with challenges in predicting agricultural output.展开更多
Yield prediction is the primary goal of genomic selection(GS)-assisted crop breeding.Because yield is a complex quantitative trait,making predictions from genotypic data is challenging.Transfer learning can produce an...Yield prediction is the primary goal of genomic selection(GS)-assisted crop breeding.Because yield is a complex quantitative trait,making predictions from genotypic data is challenging.Transfer learning can produce an effective model for a target task by leveraging knowledge from a different,but related,source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data.However,it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework.We therefore developed TrG2P,a transfer-learning-based framework.TrG2P first employs convolutional neural networks(CNN)to train models using non-yield-trait phenotypic and genotypic data,thus obtaining pre-trained models.Subsequently,the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task,and the fully connected layers are retrained,thus obtaining fine-tuned models.Finally,the convolutional layer and the first fully connected layer of the fine-tuned models are fused,and the last fully connected layer is trained to enhance prediction performance.We applied TrG2P to five sets of genotypic and phenotypic data from maize(Zea mays),rice(Oryza sativa),and wheat(Triticum aestivum)and compared its model precision to that of seven other popular GS tools:ridge regression best linear unbiased prediction(rrBLUP),random forest,support vector regression,light gradient boosting machine(LightGBM),CNN,DeepGS,and deep neural network for genomic prediction(DNNGP).TrG2P improved the accuracy of yield prediction by 39.9%,6.8%,and 1.8%in rice,maize,and wheat,respectively,compared with predictions generated by the best-performing comparison model.Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data.We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to 展开更多
The exponential growth of population in developing countries likeIndia should focus on innovative technologies in the Agricultural processto meet the future crisis. One of the vital tasks is the crop yield predictiona...The exponential growth of population in developing countries likeIndia should focus on innovative technologies in the Agricultural processto meet the future crisis. One of the vital tasks is the crop yield predictionat its early stage;because it forms one of the most challenging tasks inprecision agriculture as it demands a deep understanding of the growth patternwith the highly nonlinear parameters. Environmental parameters like rainfall,temperature, humidity, and management practices like fertilizers, pesticides,irrigation are very dynamic in approach and vary from field to field. In theproposed work, the data were collected from paddy fields of 28 districts in widespectrum of Tamilnadu over a period of 18 years. The Statistical model MultiLinear Regression was used as a benchmark for crop yield prediction, whichyielded an accuracy of 82% owing to its wide ranging input data. Therefore,machine learning models are developed to obtain improved accuracy, namelyBack Propagation Neural Network (BPNN), Support Vector Machine, andGeneral Regression Neural Networks with the given data set. Results showthat GRNN has greater accuracy of 97% (R2 = 0.97) with a normalizedmean square error (NMSE) of 0.03. Hence GRNN can be used for crop yieldprediction in diversified geographical fields.展开更多
Agricultural system is very complex since it deals with large data situation which comes from a number of factors. A lot of techniques and approaches have been used to identify any interactions between factors that af...Agricultural system is very complex since it deals with large data situation which comes from a number of factors. A lot of techniques and approaches have been used to identify any interactions between factors that affecting yields with the crop performances. The application of neural network to the task of solving non-linear and complex systems is promising. This paper presents a review on the use of artificial neural network （ANN） in predicting crop yield using various crop performance factors. General overview on the application of ANN and the basic concept of neural network architecture are also presented. From the literature, it has been shown that ANN provides better interpretation of crop variability compared to the other methods.展开更多
The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield base...The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 years.Three effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.展开更多
Agriculture plays a vital role in the Indian economy.Crop recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters.At the same time...Agriculture plays a vital role in the Indian economy.Crop recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters.At the same time,crop yield prediction was based on several features like area,irrigation type,temperature,etc.The recent advancements of artificial intelligence(AI)and machine learning(ML)models pave the way to design effective crop recommendation and crop pre-diction models.In this view,this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction(MMML-CRYP)technique.The proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop prediction.At the initial stage,equilibrium optimizer(EO)with kernel extreme learning machine(KELM)technique is employed for effectual recommendation of crops.Next,random forest(RF)tech-nique was executed for predicting the crop yield accurately.For reporting the improved performance of the MMML-CRYP system,a wide range of simulations were carried out and the results are investigated using benchmark dataset.Experi-mentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.展开更多
The prediction of crop yield is one of the important factor and also challenging,to predict the future crop yield based on various criteria’s.Many advanced technologies are incorporated in the agricultural processes,...The prediction of crop yield is one of the important factor and also challenging,to predict the future crop yield based on various criteria’s.Many advanced technologies are incorporated in the agricultural processes,which enhances the crop yield production efficiency.The process of predicting the crop yield can be done by taking agriculture data,which helps to analyze and make important decisions before and during cultivation.This paper focuses on the prediction of crop yield,where two models of machine learning are developed for this work.One is Modified Convolutional Neural Network(MCNN),and the other model is TLBO(Teacher Learning Based Optimization)-a Genetic algorithm which reduces the input size of data.In this work,some spatial information used for analysis is the Normalized Difference Vegetation Index,Standard Precipitation Index and Vegetation Condition Index.TLBO finds some best feature value set in the data that represents the specific yield of the crop.So,these selected feature valued set is passed in the Error Back Propagation Neural Network for learning.Here,the training was done in such a way that all set of features were utilized in pair with their yield value as output.For increasing the reliability of the work whole experiment was done on a real dataset from Madhya Pradesh region of country India.The result shows that the proposed model has overcome various evaluation parameters on different scales as compared to previous approaches adopted by researchers.展开更多
Crop models are widely used to predict plant growth,water input requirements,and yield.However,existing models are very complex and require hundreds of variables to perform accurately.Due to these shortcomings,large-s...Crop models are widely used to predict plant growth,water input requirements,and yield.However,existing models are very complex and require hundreds of variables to perform accurately.Due to these shortcomings,large-scale applications of crop models are limited.In order to address these limitations,reliable crop models were developed using a deep neural network(DNN)–a new approach for predicting crop yields.In addition,the number of required input variables was reduced using three common variable selection techniques:namely Bayesian variable selection,Spearman's rank correlation,and Principal Component Analysis Feature Extraction.The reduced-variableDNN modelswere capable of estimating future crop yields for 10,000,000 differentweather and irrigation scenarios while maintaining comparable accuracy levels to the original model that used all input variables.To establish clear superiority of the methodology,the results were also compared with a very recent feature selection algorithm called min-redundancy max-relevance(mRMR).The results of this study showed that the Bayesian variable selection was the best method for achieving the aforementioned goals.Specifically,the final Bayesian-based DNN model with a structure of 10 neurons in 5 layers performed very similarly(78.6%accuracy)to the original DNN cropmodel with 400 neurons in 10 layers,even though the size of the neural network was reduced by 80-fold.This effort can help promote sustainable agricultural intensifications through the large-scale application of crop models.展开更多
文摘The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine learning(ML)models effectively deal with such challenges.This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024.In addition,it analyses the effectiveness of various input parameters considered in crop yield prediction models.We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield.The total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is 125.We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers.Each study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel performance.We also discuss the ethical and social impacts of AI on agriculture.However,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven models.Therefore,thorough research is required to deal with challenges in predicting agricultural output.
基金This research was funded by the STI2030-Major Projects(no.2023ZD0406104)the Beijing Postdoctoral Research Foundation(no.2023-ZZ-116).
文摘Yield prediction is the primary goal of genomic selection(GS)-assisted crop breeding.Because yield is a complex quantitative trait,making predictions from genotypic data is challenging.Transfer learning can produce an effective model for a target task by leveraging knowledge from a different,but related,source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data.However,it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework.We therefore developed TrG2P,a transfer-learning-based framework.TrG2P first employs convolutional neural networks(CNN)to train models using non-yield-trait phenotypic and genotypic data,thus obtaining pre-trained models.Subsequently,the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task,and the fully connected layers are retrained,thus obtaining fine-tuned models.Finally,the convolutional layer and the first fully connected layer of the fine-tuned models are fused,and the last fully connected layer is trained to enhance prediction performance.We applied TrG2P to five sets of genotypic and phenotypic data from maize(Zea mays),rice(Oryza sativa),and wheat(Triticum aestivum)and compared its model precision to that of seven other popular GS tools:ridge regression best linear unbiased prediction(rrBLUP),random forest,support vector regression,light gradient boosting machine(LightGBM),CNN,DeepGS,and deep neural network for genomic prediction(DNNGP).TrG2P improved the accuracy of yield prediction by 39.9%,6.8%,and 1.8%in rice,maize,and wheat,respectively,compared with predictions generated by the best-performing comparison model.Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data.We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to
文摘The exponential growth of population in developing countries likeIndia should focus on innovative technologies in the Agricultural processto meet the future crisis. One of the vital tasks is the crop yield predictionat its early stage;because it forms one of the most challenging tasks inprecision agriculture as it demands a deep understanding of the growth patternwith the highly nonlinear parameters. Environmental parameters like rainfall,temperature, humidity, and management practices like fertilizers, pesticides,irrigation are very dynamic in approach and vary from field to field. In theproposed work, the data were collected from paddy fields of 28 districts in widespectrum of Tamilnadu over a period of 18 years. The Statistical model MultiLinear Regression was used as a benchmark for crop yield prediction, whichyielded an accuracy of 82% owing to its wide ranging input data. Therefore,machine learning models are developed to obtain improved accuracy, namelyBack Propagation Neural Network (BPNN), Support Vector Machine, andGeneral Regression Neural Networks with the given data set. Results showthat GRNN has greater accuracy of 97% (R2 = 0.97) with a normalizedmean square error (NMSE) of 0.03. Hence GRNN can be used for crop yieldprediction in diversified geographical fields.
文摘Agricultural system is very complex since it deals with large data situation which comes from a number of factors. A lot of techniques and approaches have been used to identify any interactions between factors that affecting yields with the crop performances. The application of neural network to the task of solving non-linear and complex systems is promising. This paper presents a review on the use of artificial neural network （ANN） in predicting crop yield using various crop performance factors. General overview on the application of ANN and the basic concept of neural network architecture are also presented. From the literature, it has been shown that ANN provides better interpretation of crop variability compared to the other methods.
基金supported by the Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2016-AII)。
文摘The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 years.Three effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.
文摘Agriculture plays a vital role in the Indian economy.Crop recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters.At the same time,crop yield prediction was based on several features like area,irrigation type,temperature,etc.The recent advancements of artificial intelligence(AI)and machine learning(ML)models pave the way to design effective crop recommendation and crop pre-diction models.In this view,this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction(MMML-CRYP)technique.The proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop prediction.At the initial stage,equilibrium optimizer(EO)with kernel extreme learning machine(KELM)technique is employed for effectual recommendation of crops.Next,random forest(RF)tech-nique was executed for predicting the crop yield accurately.For reporting the improved performance of the MMML-CRYP system,a wide range of simulations were carried out and the results are investigated using benchmark dataset.Experi-mentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.
文摘The prediction of crop yield is one of the important factor and also challenging,to predict the future crop yield based on various criteria’s.Many advanced technologies are incorporated in the agricultural processes,which enhances the crop yield production efficiency.The process of predicting the crop yield can be done by taking agriculture data,which helps to analyze and make important decisions before and during cultivation.This paper focuses on the prediction of crop yield,where two models of machine learning are developed for this work.One is Modified Convolutional Neural Network(MCNN),and the other model is TLBO(Teacher Learning Based Optimization)-a Genetic algorithm which reduces the input size of data.In this work,some spatial information used for analysis is the Normalized Difference Vegetation Index,Standard Precipitation Index and Vegetation Condition Index.TLBO finds some best feature value set in the data that represents the specific yield of the crop.So,these selected feature valued set is passed in the Error Back Propagation Neural Network for learning.Here,the training was done in such a way that all set of features were utilized in pair with their yield value as output.For increasing the reliability of the work whole experiment was done on a real dataset from Madhya Pradesh region of country India.The result shows that the proposed model has overcome various evaluation parameters on different scales as compared to previous approaches adopted by researchers.
基金supported by the USDA National Institute of Food and Agriculture,Hatch project 1019654.
文摘Crop models are widely used to predict plant growth,water input requirements,and yield.However,existing models are very complex and require hundreds of variables to perform accurately.Due to these shortcomings,large-scale applications of crop models are limited.In order to address these limitations,reliable crop models were developed using a deep neural network(DNN)–a new approach for predicting crop yields.In addition,the number of required input variables was reduced using three common variable selection techniques:namely Bayesian variable selection,Spearman's rank correlation,and Principal Component Analysis Feature Extraction.The reduced-variableDNN modelswere capable of estimating future crop yields for 10,000,000 differentweather and irrigation scenarios while maintaining comparable accuracy levels to the original model that used all input variables.To establish clear superiority of the methodology,the results were also compared with a very recent feature selection algorithm called min-redundancy max-relevance(mRMR).The results of this study showed that the Bayesian variable selection was the best method for achieving the aforementioned goals.Specifically,the final Bayesian-based DNN model with a structure of 10 neurons in 5 layers performed very similarly(78.6%accuracy)to the original DNN cropmodel with 400 neurons in 10 layers,even though the size of the neural network was reduced by 80-fold.This effort can help promote sustainable agricultural intensifications through the large-scale application of crop models.
