Özet:

Abstract Agriculture is the foundation of India’s economy. Farmers have a limited understanding of soil nutrient content. It is imperative to use the land that is available to the fullest extent possible by planting the right crops and using the right fertilisers. In order to produce the best results in today’s world, agriculture needs technological assistance. Traditional farming methods are being replaced by newer, more efficient methods. Fertilizer overuse is a growing concern in the modern era. To help farmers better understand soil fertility and fertiliser application amounts, various machine learning algorithms can be used. Different crops necessitate different fertiliser application amounts, and crop intake also varies. The goal of machine learning (ML) is to develop algorithms that can learn from patterns in data and then use that learning to make predictions about new data. Machine Learning (ML) techniques can effectively solve the prediction and classification problems. Because of the widespread use of machine learning in agriculture, farmers are able to overcome their greatest challenges. In this study, Support vector machine (SVM), Decision Tree (DT), and Multilayer Perceptron (MLP) are three machine learning algorithms that were used to determine how well they analyse soil nutrients. When compared to other algorithms, the results showed that MLP had a 94% accuracy rate.

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