Review on Crop Yield Prediction by Using Different Machine Learning and Deep Learning Model

Abstract

Crop yield prediction algorithms let farmers decide when and what kinds of crops to cultivate based on environmental factors, which increase yields. Predicting crop yields is difficult because of a number of intricate elements, including genotype, water availability, pest infestations, landscape quality, and harvest scheduling. By addressing these challenges through robust methodologies and innovative approaches, machine learning and deep learning models have the potential to greatly improve the accuracy and dependability of crop production forecast, thereby supporting global efforts to ensure food security and sustainable agriculture. This study utilizes Deep Learning and Machine Learning algorithms to analyse crop cultivation stages, providing solutions based on meteorological conditions, fertilizer requirements, and Growing Degree Days. Production in agriculture efficiently uses machine learning algorithms like RF, KNN, MSER, BMA, and SVM for crop yield prediction. BMA’s machine learning model has a high prediction accuracy rate of 98% for crop yields. For analysing deep learning algorithms procedure, DNN, CNN-RNN, DBN, Adaboost, and SBE algorithms are used. With an accuracy of 99.24%, DNN is the most used deep learning algorithm. Finally for evaluating the hyperparameter optimized network model, SR-BOA, GBXBLG-BOA, MLP-SMO, BDTRFNN, MARS-PCA, and RFOERNN algorithms are employed. MARS-PCA overcomes than all other techniques with accuracy of 98.7%. Experimental results consistently show that the Deep Neural Networks (DNNs) perform exceptionally well for predicting crop yield across multiple agricultural scenarios.