This analysis shows the impact of machine learning on crop yield in agriculture, suggesting ways to enhance efficiency.
Agriculture is the main source of income for Indian farmers in the developing world. The current agricultural system relies heavily on technology to produce more and earn profits, but agriculture still depends on seasonal aspects. It is very difficult to identify the type and amount of pesticide needed for plant growth. Farmers face several serious challenges, including: (i) adapting to climatic conditions due to soil erosion and pollution; (ii) soil deficiencies, such as lack of potassium, phosphorus, nitrogen and other minerals, can have a negative impact on crop yields; and (iii) given the diversity of soils in the world, farmers make serious mistakes about which crops to grow in which type of soil, which is a very important factor to understand.
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Navin Prakash (2025) studied this question.
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