SOIL ANALYSIS AND CROP RECOMMENDATION USING MACHINE LEARNING

This book summarizes the India's agriculture sector is significant. It is necessary for the Indian economy's survival and expansion. India is a significant producer of many different agricultural goods. In the process of cultivating crops, soil is crucial. A non-renewable, dynamic natural resource required for life is soil. The selection of the right crop based on the needs of the soil is a common issue faced by young Indian farmers. They experience a significant decline in productivity as a result. Earlier crop cultivation used to be done by farmers with practical experience. Based on the qualities and properties of the soil, farmers are no longer able to select the ideal crop. Therefore, a recommendation system that uses a machine learning algorithm to suggest the crop that can be harvested in that specific soil has been developed. In the proposed system, we process the user- supplied image of the soil and classify it into one of four classifications of soil: Red, Alluvial, Black, and Clay. A MobileNetV2 Architecture model accomplishes this. Several crops that can be grown in that soil type are recommended when the soil type is forecasted.

Autorentext

Dr M. Aravind Kumar uzyskä stopie B. Tech w ECE, stopie M.Tech w projektowaniu systemów VLSI w JNTUH i doktorat na Uniwersytecie GITAM w Visakhapatnam. Ma 15-letnie do wiadczenie w nauczaniu. Jest do ywotnim cz onkiem FIE, ISTE, IETE, SCIEI, UACEE i IAENG. Opublikowä 35 artyku ów naukowych w recenzowanych czasopismach i na konferencjach.

Klappentext

This book summarizes the India's agriculture sector is significant. It is necessary for the Indian economy's survival and expansion. India is a significant producer of many different agricultural goods. In the process of cultivating crops, soil is crucial. A non-renewable, dynamic natural resource required for life is soil. The selection of the right crop based on the needs of the soil is a common issue faced by young Indian farmers. They experience a significant decline in productivity as a result. Earlier crop cultivation used to be done by farmers with practical experience. Based on the qualities and properties of the soil, farmers are no longer able to select the ideal crop. Therefore, a recommendation system that uses a machine learning algorithm to suggest the crop that can be harvested in that specific soil has been developed. In the proposed system, we process the user- supplied image of the soil and classify it into one of four classifications of soil: Red, Alluvial, Black, and Clay. A MobileNetV2 Architecture model accomplishes this. Several crops that can be grown in that soil type are recommended when the soil type is forecasted.