Optimizing Crop Recommendations using Machine Learning: A Comparative Study for Enhanced Yield Prediction

For an important segment of the Indian people, agriculture serves as a primary source of income. Most Indian farmers choose to produce crops in a field using traditional farming methods; hence, one of their biggest issues is that they frequently choose to cultivate the incorrect crop for their soil type. The crop recommendation system proposed in this research would assist farmers and educate them on decision-making regarding which crops to plant on their property. Using soil parameters like potassium, nitrogen, and phosphorus as well as environmental variables like humidity, rainfall, and pH levels, to build this recommendation system, we used ML methods such as Random Forest, KNN, Naïve Bayes, SVM, and Logistic Regression. As a result, we also present comparative performance on the model for the dataset. Therefore, finally, these technologies will be helpful for farming and agriculture. Today’s smart agricultural solutions, can address the growing concern about the world population’s food consumption and environmental impact. The accuracy of this crop recommendation system will depend on the following: The quality and quantity of our dataset, the relevance and effectiveness of our features, the choice and tuning of our machine learning models, the balance of our dataset and the complexity of the crop prediction task, performing thorough training, validation, and testing will give the accuracy metric we need.