Performance Prediction for Crop Irrigation Using Different Machine Learning Approaches

Irrigation is an ancient practice that evolved over the years. Irrigation is the method through which a controlled amount of water is applied to plants making the most important recourse of irrigation. Earth is composed of 70% of water of which only 2.5% is fresh. Most of it trapped in snowfields and glaciers with only 0.007% of the earth's water for the needs of mankind. Limited water resources had become the main challenge in farming. In the chapter, machine learning algorithms and neural networks are used to reduce the usage of water in crop irrigation systems. This chapter focus on four mainstream machine learning calculations (KNN [k-nearest neighbor], GNB [Gauss Naive Bayes], SVM [support vector machine], DT [decision tree]) and a neural networks technique (artificial neural networks [ANN]) to expectation models utilizing the huge dataset (510 irrigation cases), bringing about productive and precise dynamic. The outcomes showed that k-nearest neighbors and artificial neural networks are the best indicators with the most elevated effectiveness of 98% and 90% respectively.