Real-time detection of maize crop disease via a deep learning-based smartphone app

Diseases in plants are substantially problematic for agricultural yield management. Compounded with insufficient information to correctly diagnose crops, they can lead to significant economic loss and yield inefficiencies. Due to the success of deep learning in many image processing applications, the first part of this paper involves designing a deep neural network for the detection of disease in maize due to its economic significance. A convolutional neural network is designed and trained on a public domain dataset with labeled images of maize plant leaves with disease present, or lack thereof. In the second part of this paper, the trained convolutional neural network is turned into a smartphone app running in real-time for the purpose of performing maize crop disease detection in the field in an on-the-fly manner. The smartphone app offers a cost-effective, portable, and universally accessible way to detect disease in maize. The approach developed in this paper enables recognizing early signs of plant pathogens from maize crop images in real-time in the field, thus leading to preemptive corrective actions prior to significant yield loss.