Machine Learning in Soil Borne Diseases, Soil Data Analysis & Crop Yielding: A Review

The agricultural study is critically important from a financial perspective. Research areas in agriculture are demand prediction, crop yielding prediction, soil classification, diseases, and weed detection Another dimension of agriculture is horticulture, it provides economic development, and healthy foods to society. Soil is critical for increasing crop yields, the quality of food, and its healthiness and nutritive quality. But with the right use of organic fertilizers and other modern tools and techniques, it may be enhanced. Experts use data mining and machine learning algorithms like support vector machines (SVMs), Naive Bayes (NBs), decision trees (DTs), and linear discriminant analyses (LDAs) to make predictions and draw conclusions from agricultural data (CNN). This paper comprises a review of the Machine Learning Algorithm in the soil data analysis and soil-borne diseases and Crop yielding.