Precision Agriculture through Deep Learning Algorithms for Accurate Diagnosis and Continuous Monitoring of Plant Diseases

Abstract

For sustainable food production, precision agriculture is essential, and one of its main tenets is the precise identification and ongoing surveillance of plant diseases. Conventional approaches to illness monitoring and detection are frequently labour-intensive, time-consuming, and dependent on visual inspection, which increases the risk of misidentifying diseases. Deep learning algorithms have surfaced as a potentially effective way to tackle these issues. In this study, we introduce a novel method for precision agriculture that improves plant disease diagnostic accuracy and offers continuous monitoring by utilising deep learning algorithms. Our research uses cutting-edge convolutional neural networks (CNNs) and ResNet50 to precisely identify illness symptoms in plant photos. The proposed deep learning model is trained on an extensive dataset of plant photos illustrating a range of illnesses, enabling it to identify minute visual cues that human observers might overlook. Compared to previous ML methods, the model's accuracy in detecting diseases is higher, which lowers the possibility of misdiagnosis and facilitates early intervention to minimise crop damage. By placing cameras and sensors in the fields, proposed system provides continuous monitoring in addition to precise diagnosis. The proposed deep learning model processes the real-time data and photos of the crops that are captured by these devices.