Key points are not available for this paper at this time.
The approximate average loss faced by farmers in India due to insufficient knowledge of crop suitability is around 50%, though this figure varies significantly based on the specific crop and regional factors. Plant diseases result in a substantial reduction of 15% to 25% in the potential crop yield in India. This project proposed Agrifficient (Crop recommendation, Fertilizer recommendation and Plant disease detection) system which aims to help Indian farmers select the best crops based on soil characteristics, temperature and rainfall. It helps them select suitable fertilizer and detect plant disease. It gives them the option to fetch the information in Regional languages. Agriculture is of utmost importance in the Indian economy, impacting both rural and urban livelihoods. Many farmers rely on intuition or traditional practices when choosing crops, potentially leading to financial losses. The system leverages machine learning to provide data-driven crop recommendations, improving decision-making and contributing to the stability and growth of the agricultural sector.Given the vital role of agriculture in the Indian economy, this system offers valuable guidance, helping farmers move beyond traditional practices and make informed decisions. Employing machine learning techniques, the system leverages historical data on factors like nitrogen(N), phosphorous(P), potassium(K), pH, State, City and rainfall to predict suitable crops for the current weather conditions, thereby enhancing the agricultural sector's economic growth and employment opportunities. It also aids farmers in soil quality assessment to determine what contents are high and low in the crop and recommends fertilizers, considering N, P, K and the crop for improved performance. It also detects plant disease by taking in images as input. This project is designed to empower both experienced and novice farmers by providing valuable insights into crop selection and soil management.
Gondke et al. (Fri,) studied this question.