article · Frontiers in Plant Science
Plant leaf diseases present severe risks to agricultural yields and food security, while visual inspection remains slow, inaccurate, and resource-intensive. To automate diagnosis, an efficient and lightweight deep learning architecture named DeepPlantNet has been developed. The network incorporates 28 learned layers, consisting of 25 convolutional layers and three fully connected layers, alongside batch normalisation, Leaky ReLU activations, fire modules, and mixed filter sizes. In experimental tests, the system categorised leaf diseases across ten plant conditions, including infections affecting apples, cherries, grapes, peaches, peppers, potatoes, squash, strawberries, tomatoes, and maize. The model attained average classification accuracies of 98.49 percent in an eight-class scheme and 99.85 percent in a three-class scheme. By delivering rapid and accurate disease identification from images, this architecture aims to support farmers and agricultural specialists in preventing crop destruction and limiting economic losses.
Crop diseases can cause extensive damage to food supplies and farm revenues if not diagnosed promptly. Because manual plant inspections are slow and prone to errors, automated image recognition tools provide a scalable alternative. High-accuracy automated detection helps agriculturalists intervene early, curtailing infection spread and protecting food safety in areas where specialist diagnostic infrastructure is limited.
This technology represents applied research tested on image datasets across ten specific crop diseases. It could enable automated diagnostic tools or smartphone software for farmers and agricultural professionals to identify infections in the field. However, the abstract reports only experimental classification metrics, indicating the underlying model remains at an algorithmic stage requiring integration into accessible field software or hardware before reaching commercial deployment.
AI-generated from the published abstract. Always read the original work before citing.
Introduction: Recently, plant disease detection and diagnosis procedures have 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. Method: This 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 plant disease images into many classifications. Results: The proposed approach categorizes 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 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively. Discussion: The experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.3389/fpls.2023.1212747
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
