Precision Agriculture Crop Recommendation System Using KNN Algorithm

Precision agriculture is an arising field that plans to enhance farming practices to amplify yield and limit asset wastage. One of the vital parts of precision agriculture is the improvement of harvest recommendation systems that can propose the most fitting yields and their relating development rehearses in view of different factors, for example, soil type, weather circumstances, and authentic data. In this paper, the proposal is a crop recommendation system in view of the K-Nearest Neighbors (KNN) algorithm. A background study was conducted first on various crop recommendation techniques and identified the limitations of existing systems such as poor accuracy and high computational cost. The proposed KNN-based system beats these impediments by utilizing the straightforwardness and viability of the KNN algorithm. To assess the performance of the proposed system, the data is gathered from Kaggle for different crops and utilized it to prepare and test the system. The outcomes showed that the KNN-based system beats existing strategies with regards to exactness and computational effectiveness. The recommendation system achieves an accuracy of 96% in predicting the most suitable crops based on the input parameters, which include soil type, climate, and historical data. Generally speaking, the review shows the adequacy of the KNN algorithm in creating exact and productive crop recommendation systems for precision agriculture. The proposed system can possibly alter the agriculture business by empowering farmers to settle on data-driven choices that enhance yield and limit asset wastage.