Analysis of Wheat Plant Disease Detection using Deep Learning Techniques on Real-Time UAV Images

The adverse consequences of crop diseases on agricultural yields are substantial, stemming from their propensity for widespread devastation. To provide precise protection for crops, it becomes essential to employ distinct chemical strategies when addressing various levels of disease intrusion in wheat cultivation. There is an urgent need for a quick and accurate way to assess diseases including leaf blight, loose smut, powdery mildew, and wheat yellow rust disease in order to pursue sustainable agricultural management. Modern remote sensing combined with sophisticated machine learning has sparked a growing interest in determining the severity of these diseases down to the individual leaf level of plants. However, the primary focus of our research is on the widespread field-level detection of wheat crop rust severity using deep learning networks effectively combined with multispectral data analysis from Unmanned Aerial Vehicles (UAVs). The foundation of our work is to provide real-time solution adapted to the sizable dataset gathered from UAV reconnaissance flights. Next, we utilize an approach that is multidimensional and includes the use of several methods such Convolutional Neural Networks (CNNs) and Autoencoders and CNN and Autoencoders integrated with Dense Attention Module (DAM) to train and assess the dataset's capacity to discriminate between various diseases classifications. A comparative analysis of the outcomes demonstrates the noteworthy efficacy of the suggested methodology.