Browsing by Author "UMER NURI MOHAMMED"

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    DEVELOPING IMAGE-BASED ENSET PLANT DISEASE IDENTIFICATION USING CONVOLUTIONAL NEURAL NETWORK
    (Hawassa University, 2020-11-07) UMER NURI MOHAMMED
    Nowadays, decline in food plant productivity is a major problem causing food insecurity to which plant disease is one of the factors. Early identification and accurate diagnosis of the health status of food plants is hence critical to limit the spread of plant diseases and it should be in a technological manner rather than by the labor force. Traditional observation methods by farmers or domain experts is perhaps time-consuming, expensive and sometimes inaccurate. Based on the literature, the literature suggests that deep learning approaches are the most accurate models for the detection of plant disease. Convolutional Neural network (CNN) is one of the popular approaches that allows computational models that are composed of multiple processing layers to learn representations of image data with multiple levels of abstraction. These models have dramatically improved the state-of-the-art in visual object recognition and image classification that makes it a good way for enset plant disease classification problems. For this purpose, we used an appropriate CNN based model for identifying and classifying the three most critical diseases of enset plants: - enset bacterial wilt, enset Leaf spot, and Root mealybug diseases. Enset is one of a major source of food in the South, Central and Southwestern parts of Ethiopia. A total of 14,992 images are used for conducting experiments including augmented images with four different categories; three diseased and a healthy class obtained from the different agricultural sectors stationed at Hawassa and Worabe Ethiopia, these images are provided as input to the proposed model. Under the 10-fold cross-validation strategy, the experimental results show that the proposed model can effectively detect and classify four classes of enset plant diseases with the best classification accuracy of 99.53%, which is higher than compared to other classical deep learning models such as MobileNet and Inception v3 deep learning models