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. The Ethiopian economy relies on and is exclusively dependent on agricultural-based activities. Different soil compositions (nitrogen, phosphorous, and potassium), crop alternation, soil dampness, and climate conditions play an imperative contribution in cultivation. The primary purpose of this study was to conduct a machine learning approach which can be practiced dynamically for efficient farming at a low cost. The 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. The research comprised a model that is combined with machine learning procedures (ANN, random forest, and decision tree) to know efficient and appropriate crop types. The planned model is improved through conducting deep learning methods incorporated to the existing practice for different 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 specified documents and assisting the local agronomists in forecasting different types of crop and gain benefits. In RNN, LSTM, and SVM algorithms, the accuracy is determined as 96% which is comparatively preferable as compared to other machine learning procedures under different feature and crop types. The techniques are evaluated in terms of percentage in prediction accuracy. The 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.