DEVELOPMENT OF THE COFFEE LEAF RUST SEVERITY CLASSIFIER MOBILE APPLICATION USING THE MOBILENETV2 IMAGE PROCESSING RESULT
Precise assessment of plant disease severity is essential for plant disease management. Smartphone applications can be utilized to detect, assess, and report the existence of plant diseases. Using a saved model from the image processing stage, a mobile application that shall be known as "CLR Classifier" was developed in this study to assist coffee farmers in the Province of Quirino in detecting, assessing the severity, and reporting coffee leaf rust in the coffee farms. The test result shows that the accuracy of the CLR Classifier yields an accuracy of 82.35%. The result attained indicates that the developed mobile application might be a suitable mechanism to assist farmers in the detection, severity assessment, and reporting of coffee leaf rust in coffee farms in the province of Quirino and may have great potential in disease management for modern agriculture.
Image Processing, MobileNetV2, Coffee Leaf Rust, Quirino, CLR Classifier