AI-Driven Crop Quality Assessment using Deep Learning and Image Analysis with Integrated Smart Farming Support System

Assessing Crop Quality plays an important role in the area of Agriculture as it impacts on the pricing and storage and acceptance in Market. Conventional quality inspection methods highly rely on human expertise, which may be time-consuming, subjective, and inconsistent. To overcome these, Artificial intelligence based Crop Quality Verification system by image analysis is introduced in this project. The proposed system consists of image processing techniques and deep learning models, namely Convolutional Neural Networks (CNNs) to analyze crop images and classify them as either good or poor quality crop images. The results are represented in a graphical manner showing good quality crops with a green colour and with the poor quality of crops with a red colour along with the distribution of quality in a pie-cut chart. In addition to quality classification the system provides weather updates, market prices, multiple language support and an interactive chat feature to help farmers. A fastapi-powered backend is connecting already trained model of AI with a user friendly web interface created with the help of such technologies as html, css, and java script. Transfer Learning using Pre-trained CNN model is used for extracting important features from the crop images in an efficient way in order to increase the classification performance. In total, the proposed solution addresses the issues of accuracy limitation and reduces the manual effort and can be seen as a scalable and cost-effective method for automated crop quality assessment to contribute to smart and efficient agricultural practices.