Crop Prediction Using Random Forest Classifire Algorithm

Crop Prediction Using Random Forest Classifire Algorithm

Minal Joshi, Bhakti Rathor, Snehal Borkar, Tejas Nagapure, Aftab Gawali, Prof. S. S. Deshamukh

Abstract
Since it has the second-largest population in the world and the majority of its citizens work in agriculture, India is a country that deserves our attention. Farmers cultivate the same crops over and over again without experimenting with new varieties, and they apply fertiliser in arbitrary amounts without knowing the amount and quality of the lacking ingredients. In other words, this has a direct impact on crop yield and also affects the top layer of soil and produces soil acidity. So, in order to improve the lives of farmers, we created the system utilising machine learning algorithms. Based on soil characteristics and environmental factors, our technology will recommend the optimum crop for a given piece of land. Additionally, the system offers details on the type and amount of fertiliser needed for a certain area of cultivation, as well as the seeds that are necessary. As a result, by using our system farmers can cultivate a new variety of crop, may increase in profit margin and can avoid soil pollution