An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model - Ullah, Naeem; Khan, Javed Ali; Almakdi, Sultan; Alshehri, Mohammed S.; Al Qathrady, Mimonah; El-Rashidy, Nora; El-Sappagh, Shaker; Ali, Farman (Frontiers in Plant Science, 2023)
Plant disease detection and diagnosis procedures have recently become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. As a result, this This is a provisional file, not the final typeset article research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plants diseases images into many classifications. We categorize the plant diseases into the following ten groups: Apple___Black_rot(ABR), Cherry_(including_sour)___Powdery_mildew(CPM), Grape___Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach___Bacterial_spot (PBS), Pepper_bell___Bacterial_spot (PBBS), Potato___Early_blight (PEB), Squash___Powdery_mildew (SPM), Strawberry___Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49and 99.85in the case of eight-class and three-class classification schemes, respectively. Furthermore, experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can potentially reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.