Deep learning for detection cassava leaf disease
Humairoh Ratu Ayu, Arif Surtono, Donni Kis Apriyanto
Abstract
Abstract In this research, an intelligent system for detecting cassava leaf disease has been developed by utilizing the MobileNetV2 deep learning model and displaying it using a python graphical user interface (GUI). There are five disease classes used in this study, namely Cassava Bacterial Blight (CBB), Cassava Brown Steak Disease (CBSD), Cassava Green Mite (CGM), and Cassava Mosaic Disease (CMD) and Healthy. The results showed that the overall accuracy of the test data obtained was 65,6%. The GUI application program was made to be operated efficiently for beginners and can be used by cassava farmers in the field.
Topics & Concepts
Python (programming language)Manihot esculentaGraphical user interfaceBlightDeep learningHorticultureComputer scienceArtificial intelligenceBiologyProgramming languageSmart Agriculture and AICassava research and cyanideDate Palm Research Studies