Optimizing Tomato Leaf Disease Detection through Transfer Learning and Model Compression: A Comprehensive Survey

Maximise Tomato Leaf Disease Detection by Synergistic Model Compression Techniques and Transfer Learning. Basically, it is true that as technology continues to be integrated into agriculture, methods for identifying diseases have become accurate and reliable for the management of crops. The two main such approaches that have evolved are model compression, which reduces the processing requirements of deep neural network models, and transfer learning, which reuses already pretrained models. This work investigates the impact of different transfer learning strategies on model performance and efficiency. It explains the key fundamentals of transfer learning with regard to the tomato leaf disease detection application domain and covers different kinds of pretrained models. It investigates the application of model compression techniques, such as quantization, pruning, and knowledge distillation, and provides their impact on agricultural image analysis optimization. This survey is going to put forward common challenges, clarify the pros and cons of various methods, and offer reallife examples or success stories through a comparative review of prior work.