Precision Agriculture Advancements: A Comprehensive Integrated System for Disease Prediction And Crop Yield Estimation Using Image Analysis And Environmental Data

The primary problem facing agriculture, which is essential to ensuring the world's food security, is maximizing crop productivity while reducing the effects of plant diseases. Advanced technologies have the potential to completely transform agricultural methods, particularly in the parts of computer vision and machine learning. This study uses meteorological datasets and fruit image analysis to create an integrated agricultural decision support system for crop yield estimation and disease prediction. By offering early plant disease detection and precise crop yield estimates, the system seeks to improve precision agriculture techniques. A variety of datasets with plant photos labelled with disease information are gathered for the study, and meteorological data is integrated to capture environmental variables. The technology includes advanced image processing techniques to extract relevant features from plant pictures. The suggested method analyses images using a convolutional neural network technique to forecast the disease in impacted fruits. Make recommendations for natural fertilizers based on the ailment being suffered. The Multilayer Perceptron algorithm is used to train the model using a large dataset that contains historical meteorological data, allowing it to identify patterns and connectionsbetween environmental conditions. Lastly, farmers receive an SMS notice with prediction specifics.