Transfer Learning Models Used in the Classification of Plant Leaves Disease: A Review (JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences, 2024)
In many countries around the world, agriculture plays a crucial role due to rapid population growth and the resulting increasing demand for food. Therefore, there is an urgent need to improve crop quality, which has a clear impact on increasing the economic and financial growth of farmers. Important factors contributing to the decline in crop quality are diseases caused by bacteria, viruses, fungi and other agricultural pests. The impact of these diseases can be mitigated using plant disease detection techniques based on artificial intelligence techniques. Transfer learning models in such cases are particularly useful for early identification and detection of these diseases, as they are specifically data-centric and prioritize specific outcomes related to the task at hand. This study provides a comprehensive overview of the different stages of the general plant disease detection system and a comparative analysis of the temporal model used to classify plant diseases. This analysis aims to enhance agricultural economic growth and provide tangible benefits to farmers and agricultural businesses, which have a direct impact on the financial and economic income of countries.