Precision Agriculture: Deep and Machine Learning Approaches for Crop Disease Predictions

Agriculture, a vital sector shaping India's economy, serves as the cornerstone of food production. This study focuses on estimating crop yield by integrating environmental, soil, water, and crop factors. Despite significant efforts in yield prediction, challenges persist, including the effective mapping of raw data to crop yield values and the reliance on feature quality for model performance. To address these challenges, we employ deep learning models for meaningful crop feature extraction. The system utilizes a dataset repository and incorporates diverse classification algorithms such as random forest and convolutional neural networks. Our experimental results highlight the system's accuracy, precision, recall, and scores, providing valuable insights into the agricultural forecasting domain. Through the integration of deep learning techniques and diverse classification algorithms, we successfully estimate crop yield and identify factors impacting agricultural productivity. The findings underscore the importance of leveraging advanced computational methods for crop yield estimation and disease diagnosis, facilitating informed decision-making in agriculture and contributing to sustainable food production and economic growth.