AI-Powered Crop Suggestion, Yield Prediction, Disease Detection, and Soil Monitoring

India’s agricultural sector is crucial for food security. However, factors such as climate change, soil quality, and pest infestations can significantly impact crop yields. This research aims to develop a machine learning-based model to predict crop yields accurately. The proposed model leverages advanced techniques like Inception ResNet V2, AlexNet, Random Forest, Decision Tree, KNN, Naive Bayes, Simple RNN, Logistic Regression, and Voting Classifier to analyze historical data and identify key factors influencing crop yield. By considering variables such as area, crop variety, and climatic conditions, the model provides valuable insights to farmers. The proposed model achieved a high accuracy of 91%, demonstrating its potential to significantly improve agricultural practices. By enabling farmers to make informed decisions regarding crop selection, planting, and resource allocation, this research contributes to sustainable agriculture and food security.