Investigation and classification of soil using distance method and Crop Prediction

Farming is a crucial source of livelihood for the people of India, and agriculture plays a significant role in the country’s economy. However, one of the major challenges faced by farmers is the uncertainty of rainfall and the lack of reliable crop prediction mechanisms. This article aims to address this issue by studying and identifying an effective method for analyzing and classifying soil, thereby assisting farmers in selecting suitable crops. Our proposed dissimilarity method efficiently identifies similarities among soil samples. Additionally, if farmers are unable to pursue the initially recommended crop due to personal reasons, our method also suggests the best alternative crop that is suitable for the same soil. The methodology relies on a dataset obtained from the local soil testing laboratory in Buldhana District, Maharashtra. By employing the Bray Curtis algorithm on this dataset, the paper successfully achieves its objective of identifying similarities and dissimilarities in soil composition based on nutrient content, regardless of soil type. However, one of the major challenges in this research area is the acquisition of accurate data. To address this, it is necessary to conduct extensive primary data collection and utilize the latest available secondary data for analysis, ensuring more precise and reliable results. The outcome the research will support the farming system in the scope.