Weather and Crop Yield Prediction by Machine Learning Model and Spiking Neural Network

Abstract

This paper, "Weather and Crop Yield Prediction by Machine Learning Model and Spiking Neural Network," is to improve crop yield prediction efficiency by utilizing cutting-edge technology and transform agricultural practices. The creation of two prediction models specifically for this purpose is the main goal of the study. The first model incorporates geographical information such as Normalized difference vegetation index (NDVI), Standard Precipitation Index (SPI), and Vector Choquet Integral (VCI) and uses a Genetic Algorithm to reduce the size of the input dataset. This reduction attempts to lessen learning process confusion brought on by comparable data with different values. The model improves its capacity to identify pertinent patterns for precise yield estimates by incorporating spatial data. The SoftMax function is employed by the second model, a Modified Convolutional Neural Network (MCNN), as an error correcting method. By reducing errors and raising the overall accuracy of the model, the SoftMax function improves the output and produces forecasts that are more accurate. The study emphasizes how crucial geographic data including satellite imagery is for supplying crucial insights for predicting crop yields. Farmers can make well-informed decisions by having a thorough understanding of agricultural dynamics, which is facilitated by spatial patterns and temporal shifts. Statistical and geographic data are used to completely study the effects of numerous variables, such as biological, economic, and environmental ones, on crop yield production. The goal of the project is to create precise and effective instruments that enable farmers to make knowledgeable decisions about their soil by combining machine learning with spiking neural networks.