Machine Learning Based Sorghum Disease Detection: In The Case Of North Wollo Zone Raya Kobo In Amhara Region

Agriculture is the main source of prosperity for most of the countries and their economic growth. Therefore, plant diseases and infections spread in the plant affect the quantity and quality of the plant, which makes it a threat to food security. Sorghum is the most important food security crop at the global level including Ethiopia. Objectives: The aim of this paper is to design and develop a model using machine learning technique to detect sorghum diseases and to analyze the efficiency of convolutional neural networks (CNNs) for the purpose of enhancing the accuracy in detecting and classifying various sorghum diseases. Methods: Image processing and Deep learning algorithms can be used to identify the disease of sorghum to know whether the leaf is affected or not at an early stage. The proposed system is initially started on the collected sorghum leaf image and then an image resized into 256 x 256 pixels to decrease the computational burden. The resized RGB images are processed using various Image enhancement techniques and then the processed sorghum leaf image datasets are segmented using Threshold algorithm. Totally we collect 3141 image datasets from the selected places and 80 % of the dataset (2512 images) are used for training while the remaining 20 % of the datasets are used for testing purpose. Then, the sorghum leaf Detection model is trained using Convolutional Neural Network algorithm. Findings: The Model training accuracy and the model loss is 90.62% and 0.1863 respectively, The Model testing accuracy and the model loss is 99.94% and 0.0023 respectively with 50 numbers of epochs, and finally the model takes the image as input and predicts the class of the image<br>