Fruit Quality And Defect Classification Using Deep Learning

Agriculture emphasizes the need to automate quality improvement and enable uniform defect classification due to high efficiency for that matter. This is particularly due to the cases of inefficient practices notable in the procedures now in place. According to this thesis, we assert an innovative approach introducing the application of the YOLOv7 deep learning model to automate the process of identifying defects and classifying quality in fruits with around 88% accuracy. The YOLOv7 model, which is effective in object detection and subsequent classification, has been optimized to detect a wide range of fruits. This is critical in ensuring that the evaluations can be uniform hence trustworthy. Moreover, the model training time has been reduced using simpler computers hence more feasible when handling many data. This project draws data from many fruits to ascertain different quality ranges. Therefore, the project utilizes deep learning approaches including Convolutional Neural Networks to identify and learn essential features from the images. With extensive training and validation, which sometimes includes data expansion, the model is significantly enhanced for its generalization and robustness. Quality assessment using the YOLOv7 model in automation reflects considerable gain. The model has a practical implementation with an accuracy level of around 79.6%. The model minimizes errors and enhances uniform evaluation by reducing the dependence on manual workers. Farm input will, thus, experience high output through this automation system with low entry and economic results.