Review on Different Approaches for Crop Prediction and Disease Monitoring Techniques

In this paper, distinct techniques for predicting crop yield have been discussed. Methods such as artificial neural network, Deep Learning based Weighted SOM, and Adaptive neuro-fuzzy modelling (ANFM), Multilayer Feed-Forward Neural network (MFFNN) are reviewed to determine the finest techniques for predicting crop yield. Crop yield prediction depends on natural factors such as fertilizers, pesticides, water quality, spacing etc., as well as external factors like heat, moisture and precipitation etc., which affects the crop yield. Different models have been studied to obtain optimal range for various factors. Also, Disease detection plays crucial role to improve crop yield so its early identification is very important. This paper delivers comprehensive data about the distinct models for crop yield prediction. Also, different techniques for detecting diseases in plants are reviewed.