Analyzing Soil to Recommend Crop and Plant Health Monitoring

Abstract

The backbone of India’s economy and jobs is agriculture, which is strongly dependent on it. Poor crop selection is a major cause of agricultural sector losses, which are made worse by farmers’ ignorance of the nutrients, minerals, and moisture content of the soil. Using cutting-edge technology like machine learning and deep learning, it may be possible to address this problem by creating a model that recommends the optimal crop determined by weather and soil data.Farmers have historically been in charge of choosing crops, managing their development, and, using their knowledge, deciding when to harvest them. Farmers have always depended on their hands-on experience to manage their crops. Rapid environmental change, however, brings with it new difficulties. With deeper accuracy in crop result forecasting than standard prediction techniques, deep learning approaches are gradually taking the place of the former. In order to maximize the performance of these models and assist farmers in improving agricultural productivity and adapting to changing environmental conditions, effective feature selection approaches are essential. Rapid environmental change, however, presents difficulties for the agricultural community of today. Therefore, deep learning approaches are gradually taking the place of conventional prediction methods in the more accurate calculation of agricultural productivity. Techniques for selecting features effectively are crucial.