Prediction of Crop Yield by Support Vector Machine Coupled with Deep Learning Algorithm Procedures in Lower Kulfo Watershed of Ethiopia

Abstract

Sensible and judicious utilization of water for agriculture in conjunction with prediction techniques increases the crop yield. Te Ethiopian economy relies on and is exclusively dependent on agricultural-based activities. Diferent soil compositions (nitrogen, phosphorous, and potassium), crop alternation, soil dampness, and climate conditions play an imperative contribution in cultivation. Te primary purpose of this study was to conduct a machine learning approach which can be practiced dynamically for efcient farming at a low cost. Te support vector machine (SVM) was applied as a machine learning procedure, whereas long short-term memory (LSTM) and the recurrent neural network (RNN) were considered as deep learning procedures. Te research comprised a model that is combined with machine learning procedures (ANN, random forest, and decision tree) to know efcient and appropriate crop types. Te planned model is improved through conducting deep learning methods incorporated to the existing practice for diferent crop condition. Pure data and related evidence are attained concerning the quantities of soil constituents desired through their expenditures distinctly. It delivers well precision as compared to the current model examining the specifed documents and assisting the local agronomists in forecasting diferent types of crop and gain benefts. In RNN, LSTM, and SVM algorithms, the accuracy is determined as 96% which is comparatively preferable as compared to other machine learning procedures under diferent feature and crop types. Te techniques are evaluated in terms of percentage in prediction accuracy. Te results generated are important for agrarians, experts, researchers, and local farmers to maximize the crop productivity and help to enhance agriculture and climate change-related decisions, especially in low-to-middle-income countries.