FASAL FUSION: An Approach to Transform Crop Recommendations

In today's world of technology, farmers in several parts of India still face crop production problems due to soil and climate. No guide is available to assist them in choosing the appropriate types of plants that should be grown. The elevated suicide rate among Indian farmers, with 154 daily cases, is largely driven by “family problems” and “illness,” as reported in the 2022 NCRB annual study [1]. In this paper, a Crop Recommendation System that recommends a crop which is best suited for the soil by keeping Nitrogen (N), Phosphorus (P), Potassium (K), Humidity, Rainfall, Temperature, and pH as the main factors is proposed. It is a combination of a robust system for soil analysis, optimization and recommendation algorithms, to recommend crops. This approach has the potential to significantly improve the accuracy and efficiency of soil quality assessment and nutrient analysis. It can assist farmers in making informed crop choices, optimizing resource utilization, and enhancing overall agricultural productivity. The user inputs the values of N, P, K, humidity, rainfall, temperature, and pH, based on these values the selected Machine Learning algorithms have been used for the best crop prediction. The Random Forest Algorithm gives us the best results with 99.24% accuracy among Decision Tree Classifier (DTC), K-Nearest Neighbour Algorithm (KNN), Random Forest (RF), Support Vector Machine Algorithm (SVM), Logistic Regression Algorithm (LR), and AdaBoost Algorithm.