Artificial intelligence driven crop suitability mapping – Optimizing horticulture for the future

Abstract

One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography. For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture. By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers. This data-driven approach improves decision-making accuracy, minimizes environmental risks, and
aids farmers in adapting to evolving conditions. The role of AI in crop suitability mapping is particularly important for horticultural crops, which are highly sensitive to specific soil and climate requirements. This article explores the concept of crop suitability mapping, the role of AI in enhancing its precision and efficiency, and how AI is transforming the modern agriculture.