A Brief Overview of Deep Learning based Techniques for the Detection of Wheat Leaf Disease: A Recent Study

Wheat is an important cereal crop and is the second most consumed cereal after rice globally. It is a staple food for more than one-third of the world’s population. The production of wheat depends on various factors, such as climate, temperature, soil, pests, bacteria, and other biotic and abiotic factors. However, diseases can have a significant impact on wheat production. Various wheat diseases can affect crop yields,including leaf rust, leaf spot, spike infection, virus, bacterial back chaff, bacterial spike blight, and aphids. Leaf rust, in particular, is known to cause severe damage to wheat leaves. To combat these diseases, researchers have been investigating the use of advanced technologies such as deep learning and image-processing approaches for plant disease recognition. The process of disease detection involves several steps, including image preprocessing, segmentation, feature extraction, and classification. The accuracy of these steps directly affects the reliability and accuracy of the classification algorithms used to identify plant diseases. Recent studies have reviewed the state-of-the-art techniques used in this context and evaluated their effectiveness in real life applications. Overall, the use of advanced technologies such as deep learning and image processing for disease detection in wheat crops holds great promise in improving crop yields and reducing losses due to diseases. By identifying diseases at an early stage, farmers can take appropriate measures to control the spread of the disease and prevent significant crop damage. This research work presents a comparative analysis of the state-of-the-art work that has been carried out in this context and the effectiveness of the techniques in real-time.