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Advancing Agricultural Product Grading with Deep Learning Techniques
Through the implementation of transfer learning strategies inside deep learning frameworks, the objective of this project is to implement a transformation in the tomato quality classification process. By classifying tomatoes, guavas, and lemons into a variety of separate groups according to their quality and the flaws that have been detected (such as defectfree, cracks, pests, skin cracks, sunburn, and end rot), the project intends to overcome the limitations that are associated with traditional classification methods. Using a dataset that has been rigorously curated and verified, this research presents a novel technique that makes use of neural networks that have already been trained in order to achieve extraordinary accuracy, efficiency, and scalability in the evaluation of tomato quality. Through this work, there is the potential to make substantial advancements in agricultural produce evaluation procedures.
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