mPD-APP: a mobile-enabled plant diseases diagnosis application using convolutional neural network toward the attainment of a food secure world - Asani, Emmanuel Oluwatobi; Osadeyi, Yomi Phineas; Adegun, Adekanmi A.; Viriri, Serestina; Ayoola, Joyce A.; Kolawole, Ebenezer Ayorinde (Frontiers in Artificial Intelligence, 2023)
The devastating effect of plants disease infestation on crop production poses a significant threat to the attainment of the United Nations' Sustainable Development Goal 2 (SDG2) of food security, especially in Sub-Sahara Africa. This has been further exacerbated, by the lack of effective and accessible plant disease detection technologies. Farmers' inability to quickly and accurately diagnose plant diseases leads to crop destruction and reduced productivity. The diverse range of existing plant diseases further complicates detection for farmers without the right technologies, hindering efforts to combat food insecurity in the region. This study presents a web-based plant diagnosis application, referred to, in this study, as mobile-enabled Plant Diagnosis-Application (mPD-App). First, a publicly available image dataset, containing a diverse range of plant diseases was acquired from Kaggle for the purpose of training the detection system. The image dataset was then made to undergo the pre-processing stage which included processes such as Image to Array Conversion, images reshaping, data augmentation. The training phase leverages the Convolutional Neural Network (CNN)’s vast computational ability to effectively classify image datasets. The CNN model architecture featured six convolutional layers (including the fully connected layer) with phases such as Normalization layer, Rectified Linear Unit (RELU), Max pooling layer and Dropout layer. The training process was carefully managed to prevent underfitting and overfitting of the model, ensuring accurate predictions. The mPD-App demonstrated excellent performance in diagnosing plant diseases, achieving an overall accuracy of 93.91%. The model was able to classify 14 different types of plant diseases with high precision and recall values. The ROC curve showed a promising Area Under the Curve (AUC) value of 0.946, indicating the model's reliability in detecting diseases. The web-based mPD-App offers a valuable tool for farmers and agricultural stakeholders in Sub-Sahara Africa to detect and diagnose plant diseases effectively and efficiently. To further improve the application's performance, ongoing efforts should focus on expanding the dataset and refining the model's architecture. Agricultural authorities and policymakers should consider promoting and integrating such technologies into existing agricultural extension services to maximize their impact and benefit the farming community.