Convolutional and Spiking Neural Network Models for Crop Yield Forecasting

G Karuna,K Pravallika,K Anuradha,V Srilakshmi,S Tummala,S Kosaraju,S Singh,P Bobba

doi:10.1051/e3sconf/202130901162

Abstract

Prediction of Crop yield focuses primarily on agriculture research which will have a significant effect on making decisions such as import-export, pricing and distribution of specific crops. Predicting accurately with well-timed forecasts is important, but it is a difficult task due to numerous complex factors. Mostly crops like wheat, rice, peas, pulses, sugar cane, tea, cotton, green houses, corn, and soybean can all be used to forecast crop yields. We considered corn dataset to predict the yield for 13 different states in United States. Crop development and progression are strongly affected by climatic changes and unpredictability. Predicting crop yield well before harvest time will support farmers for selling and storing their crops. Agriculture involves large datasets and knowledge processes. Factors such as Weather Components, Soil Components, Management practices, genotype and their interactions are used in predicting Corn Yield. Precise crop growth generally necessitates a complete overview of the functional correlations between yield and all these interactive variables, which necessitates the use of large datasets and complex algorithms to demonstrate. Various Machine Learning models, Deep Learning models, and Artificial Neural Network algorithms are used for predicting. Deep Neural Network Models such as Convolution Neural Networks (CNN), Spiking Neural Networks (SNN), and Recurrent Neural Networks (RNN) are used to assess corn yield. Integrating CNN, RNN and SNN models outperformed than individual model performance.

Highlights

Crop yield forecasting plays a vital role for global food Production
What crops need to be grow and what crops cannot grow need to be considered based on the past years experiences and can gather information from the companies like Syngenta[2] which is a global supplier of crop protection products like, seeds like (Rice and Corn) and other related products
We show that using corn yield data from the Midwest of the United States, this model outperformed both traditional statistical methods and entirely nonparametric neural networks in predicting yields of years denied during model training

Summary

Introduction

Crop yield forecasting plays a vital role for global food Production. In order to breed better varieties for different types of environments, seed companies must forecast new hybrids. With the help of crop predictions farmers can be benefited to avoid the losses financially and can be known before what crop should be grown in which season and what are the precautions need to be taken according to the environment and soil interactions. Seed companies as well as farmers are going to get benefited with the help of forecasting. For example, is a type of phenotype trait as shown in above Fig and below Fig 2

Objectives

Results

Conclusion