Crop Recommendation Using IoT and ML
Meet Senjaliya, Manan Shah, Meru Shah, Dr.Vaishali Wadhe
Department of Artificial Intelligence and Data Science
K.J Somaiya Institute of Technology
Mumbai, India
Abstract—Certainly, with the constraints of limited space on domestic lands, the strategic selection of crops, taking into account the unique factors in the chosen area and the cur- rent demand, has become an essential consideration Modern agriculture is witnessing a paradigm shift as data-driven solutions intersect with traditional farming practices. This abstract presents an innovative project that revolutionizes crop cultivation and contributes to India’s agricultural and economic welfare. By leveraging a robust database and advanced analytic, the project empowers farmers to make well-informed decisions regarding crop selection and production. The project’s primary objective is to balance the demand and supply of crops by offering farmers insights into optimal crop choices. Through a comprehensive analysis of factors such as climate data, soil conditions, historical yields, and market trends, the project assists farmers in cultivating crops that match real-world demands. This strategic approach not just enhances crop cultivation, but also helps reduce the likelihood of unforeseeable results.
A notable outcome of the project is its ability to regulate crop production effectively. Through the project’s predictive modeling and rigorous calculations, the program tackles concerns related to both excessive production and scarcity. Consequently, market prices become more stabilized, creating favorable conditions for both farmers and consumers. At its core, this project epitomizes the synergy between technology and agriculture, promoting sustainable practices and resource allocation. As India continues its quest for food security and economic prosperity, the integration of data-driven insights into farming practices emerges as a transformative force. By bridging the gap between traditional wisdom and technological advancement, the project paves the way for optimized crop growth, prudent resource utilization, and enhanced economic stability on a national scale.
Index Terms—Yield forecasting, Data-driven agriculture, Crop productivity, Fertilizer recommendation, Crop pricing, Machine learning, SVM, RANDOM FOREST.