Detection and classification of diseases in plant leaves using deep learning methods

With the increase in the world population day by day, the need for food also increases in parallel. For this reason, it is an important area of interest to increase and improve the quality of crop yield with the increase in the need for agriculture. If the plants encounter a disease, the yield will decrease with the decrease in the amount and quality of the crop to be obtained. In this regard, the quality of products obtained from plants depends on the protection of plants from diseases. When a disease occurs in the plant, early detection and treatment of the disease is an essential requirement, and thus, the decrease in the quality of the product to be obtained from the plant will be prevented. In this study, classification was made using deep learning-based models and various publicly available datasets, which have been very popular in recent years, in order to detect diseases at an early stage. With the advances in agricultural technology and the use of artificial intelligence in the diagnosis of plant diseases, it is becoming increasingly important to conduct appropriate research for sustainable agricultural development. A study to detect diseases in plants early and to increase the amount of product to be obtained is important. Because it can be difficult and misleading to detection the disease by an expert or a farmer with traditional classification methods such as naked eye or laboratory testing, and also because it has many limitations, such as time-consuming, costly and subjective, it provides a quick and accurate way to detect and classify plant diseases using deep learning methods. For this purpose, deep learning architectures accepted in the literature were used to detect diseases in plant leaves early. In addition, a new model has been developed to achieve more successful results. KEYWORDS: Plant Diseases, Deep Learning, Image Processing, Convolutional Neural Network, Classification, Artificial Intelligence