Tomato Plant Leaf Disease Detection Using Transfer Learning VGG16

Agriculture is a crucial industry to humankind's continued existence. Simultaneously, digitalization's pervasive influence made it simpler to accomplish previously challenging jobs in a wide range of disciplines. The agriculture industry, for both the farmer and the consumer, would greatly benefit from technological and digital adaptation. Through the use of technology and consistent monitoring, illnesses can be detected early on and removed, resulting in a higher yield. The economic, social, and political lives of farmers and the entire agricultural industry are profoundly impacted by the health and productivity of their crops. Therefore, in order to detect the illnesses at the proper moment, it is essential to conduct careful monitoring at different phases of crop growth. However, humans may require more than their natural attire, and there may be situations when doing so would be deceiving. Accurate identification requires a system that can automatically recognize and categories the numerous illnesses that can affect a given crop. The current proposed framework was inspired by this train of thought. The suggested framework is primarily concerned with the transfer learning phenomena based on VGG16, and the “Plant Village” dataset, which contains both damaged and healthy potato and tomato leaves, is being explored for implementation.

I. Introduction

When thinking about the economy of India or other developing countries, agriculture is an essential sector. It stresses the significance of attentive plant care from germination to harvest. Weather conditions, crop survival against numerous illnesses, and animal survival are just a few of the many challenges the crop faces along the way to fruition. The issue can be remedied if the field is adequately protected so that the crops are not damaged by the numerous animals during these crucial times [1]–[3]. The second major issue is the weather, which is not under human control and can only behoped for in order to produce better crops. Preventing the spread of diseases that could stunt the crop's development and reduce its yield is the top priority. If these diseases are caught in time, the crop can be protected using the correct fertilisers. The agricultural community would benefit greatly from a digitalized disease identification and classification system. This will shorten the time it takes to identify diseases and improve the accuracy of disease classification.