Agriculture Intelligence and Support System for Farmer

Abstract

In numerous developing nations, agriculture stands as the primary source of income. Contemporary farming methods are consistently advancing to meet the demands of an ever-changing planet. Farmers encounter difficulties, such as coping with climate fluctuations due to soil erosion and industrial emissions. The deficiency of essential minerals like potassium, nitrogen, and phosphorus in the soil can lead to diminished crop growth, presenting a challenge for farmers to meet the evolving expectations of merchants and customers. Confronting  these challenges necessitates innovative approaches. This research paper delves into the application of machine learning techniques, with a specific focus on the Support Vector Machine (SVM) and Random Forest algorithms, for predicting crop yields. This predictive modelling assists farmers in optimizing resource allocation and making informed decisions about crop production. The paper underscores the importance of accurate crop prediction for ensuring sustainable and efficient farming practices. It highlights the drawbacks of conventional methods and introduces machine learning as a viable alternative. The SVM and Random Forest algorithms are scrutinized in detail, elucidating their underlying principles and suitability for crop prediction.