Cropsight: Enhanced Crop Prediction using IoT and Machine Learning

As agriculture moves into a technology-driven era, the relevance and popularity of smart farming solutions is increasing rapidly. In today’s agricultural landscape, where farmers are faced with unreliable weather and soil conditions, traditional methods are often ineffective for successful crops and farms, therefore, crop selection becomes inefficient, leading to lower yields and waste of inputs. This paper presents Cropsight, an IoT-based crop recommendation machine learning system. The system uses sensors to measure soil nutrients - Nitrogen, Phosphorus, and Potassium (NPK), and measures pH, temperature, humidity, and moisture. These values are evaluated using the K-Nearest Neighbor (KNN) algorithm to predict the best crops for the present growing conditions. Using a web-based dashboard with React.js, it provides a means to monitor the soil parameters and crop recommendations in real time. With this methodology, Cropsight optimizes farm input outputs, enables data-driven decision making, and helps generate higher yields and sustain more sustainable farming practices.