Crop Yield Prediction Using Adam Optimizer and Machine Learning

Abstract

The data in its form generated from variable sensors incredibly impacts the structure of the functional structure utilizing Machine Learning (ML) calculations. Many of its components are utilized to work on all areas of the rice harvesting process in horticulture, which change customary rice cultivating tests into another period of smart rice horticulture or accuracy in rice farming. Here played out a study of the most recent examination on keen information handling innovation applied in farming, especially in crop yield forecast. Artificial Intelligence (AI) is a rule based unbiased calculation model for significant prediction on paddy and crop horticulture. This played out a Systematic Literature Review (SLR) to extricate and blend the calculations and elements that have been utilized in crop yield expectation studies. In view of pursuit standards, recovered 567 important examinations from six electronic information bases, of which have been chosen 50 investigations for additional investigation utilizing incorporation and avoidance measures. Examined these chose concentrates cautiously, investigated the techniques and elements utilized, and gave ideas to additional exploration. As indicated by examinations, the most utilized highlights are temperature, precipitation, and soil type, and the most applied calculation is Artificial Neural Networks in these models. As indicated by this extra examination, Convolutional Neural Networks (CNN) is the deep learning architecture that involves the immense layers of calculations on the investigation results and improving the forecast results.