Data-Driven Farming: A Modern Approach to Crop Management Using IoT and ML Technologies

Abstract

The ever-growing evolution of digital technologies has brought forth new prospects to add value to the agricultural activities, making them more efficient, accurate and environmentally friendly. The project, entitled Revolutionizing Farming with Machine Learning and IoT: A Smart Agriculture Approach, introduces an integrated framework that integrates real-time sensing with intelligent data analysis to help farmers make better decisions. The system employs an IoT module based on ESP32 connected to other sensors, including soil moisture, water level, temperature and humidity, LDR, and gas sensors, and an OLED display to monitor locally. The field data is constantly collected by these devices and used to assess the condition of crops, efficient irrigation, and the avoidance of needless use of resources. Along with the use of IoT-based monitoring, machine learning techniques are introduced to predict crops, estimate yields, recommend fertilizers, forecast weather, and detect plant diseases using image processing. Unlike traditional approaches that depend mainly on manual reports or limited datasets, the proposed solution integrates multiple functions into a single intelligent system. It also includes a digital marketplace that enables direct communication between farmers and consumers, improving transparency and income opportunities. Overall, the system highlights how the combination of IoT and machine learning can enhance productivity, support sustainable farming, and improve decision-making while minimizing environmental impact.