Abstract:
Accurate and timely identification of pests and diseases is critical for effective pest and disease
management; however, information and expert consultations may not be readily available to
farmers and may be expensive. Mobile phone technology with cloud computing Artificial
intelligence disciplines have advanced significantly over the years, making mobile phones with
cameras widely available to people at lower costs and easily usable for identifying plant pests and
diseases, surpassing the constraints of conventional techniques. The effect of using mobile phone
cameras and phone camera-attached lenses to train an Artificial Intelligence (AI) engine to
identify cinnamon leaf spot disease was experimented under several conditions. Three
smartphone cameras (64 MP, 48 MP, and 8 MP), two camera attached lenses (10x and 30x
magnifications), with or without flashlights, and two sides of the leaf (upper and lower) were
taken as different conditions to make 24 treatment combinations. Fifty images of diseased leaves
and 50 images of healthy leaves were obtained under each combination and image processing
engines were trained for each combination by the open-source application called “Teachable
Machine” by uploading images of diseased leaves and healthy leaves for each class. Then engines
developed were validated with 10 healthy and 10 images with diseased leaves captured from a
9.5 MP camera under the same treatment combinations. Results revealed that the quality of the
camera, AI lens, and flashlight conditions used to take the images did not affect the accuracy of
identification by the engine. The trained engines could be deployed to develop a mobile-based
disease diagnosis app for field use.
