Implementation of Crop Selection by Land Dataset Using Machine Learning

Gayathri U and Dr. Priya Anand
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Gayathri U: Vels Institute of Science , Technology and Advanced Studies, India
Dr. Priya Anand: Vels Institute of Science , Technology and Advanced Studies, India

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 4, 480-482

Abstract: As we are aware of the fact that, most of Indians have agriculture as their occupation. Farmers usually have the mind-set of planting the same crop, using more fertilizers and following the public choice. By looking at the past few years, there have been significant developments in how machine learning can be used in various industries and research. So, we have planned to create a system where machine learning can be used in agriculture for the betterment of farmers. India is an Agricultural Country and its economy largely based upon crop productivity. So we can say that agriculture can be pillar of all business in our country. Selecting of always crop is very important in the agriculture planning. Many researchers studied guess of yield rate of crop, guess of weather, soil categorizing and crop classification for agriculture planning using machine learning techniques. Many changes are required in the agriculture department to improve changes in our Indian economy. We can improve agriculture by using machine learning system which are applied simply on farming sector. Along with all advances in the machines and technologies used in farming, functional information about different matters also plays a significant role in it. The concept of this paper is to implement the crop selection method so that this method helps in solving many agriculture problems. This enhances our Indian wealth by maximizing the yield rate of crop production. In our project crop is predicted by algorithm namely Recurrent Neural Network (RNN) as proposed and Random Forest (RF) as existing and its accuracy is calculated and compared with other algorithms.

Date: 2025
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