Plant Leaf Disease Detection Using Metaheuristic Optimization Algorithms and Deep Learning

• Published in: Revue d'Intelligence Artificielle Vol. 38; no. 2; p. 531
• Main Authors: Subbiah, Priyanga, Krishnaraj Nagappan
• Format: Journal Article
• Language: English; French
• Published: Edmonton International Information and Engineering Technology Association (IIETA) 24.04.2024
• Subjects: Accuracy; Agricultural production; Algorithms; Artificial intelligence; Classification; Computer vision; Crop production; Datasets; Deep learning; Heuristic methods; Illnesses; Leaves; Machine learning; Medical diagnosis; Neural networks; Optimization; Organisms; Plant diseases; Signs and symptoms
• ISSN: 0992-499X; 1958-5748
• DOI: 10.18280/ria.380216

Plant diseases significantly reduce the yield and the production of crops across the globe. Crop productivity, plant development and human access to food have all been hampered by the prevalence of plant diseases throughout the history. In general, leaves exhibit symptoms if the plant is affected by diseases. Therefore, it is essential to identify the type of infestation to reduce the destructiveness of the disease. This scenario allows one to replicate the spread of infectious diseases and the inability of farmers to recognize and remember them. One possible approach to tackle this issue is to utilise Deep Learning (DL) techniques in conjunction with Machine Learning (ML) approaches within the domain of Computer Vision (CV). The current research has introduced the APLDD-ESOSDL approach, which utilises deep learning to optimise the search for symbiotic organisms in order to automate the detection of plant leaf diseases. The objective of the proposed APLDD-ESOSDL approach is to enhance agricultural yields and reduce crop losses by offering farmers a visual depiction of disease symptoms. The goal of the APLDD-ESOSDL approach is to accurately classify the presence of leaf diseases. The APLDD-ESOSDL technique utilises the inception ResNet-v2 model as a feature extractor and the Stacked Long Short-Term Memory (SLSTM) model for classification. In addition, the hyperparameters of the SLSTM algorithm are adjusted using the Enhanced Symbiotic Organism Search (ESOS) approach. A comprehensive experiment was carried out utilising the reference data set to verify the effectiveness of the APLDD-ESOSDL approach. The APLDD-ESOSDL algorithm outperformed more advanced systems, achieving a maximum accuracy of 99.22%, precision of 98.52%, sensitivity of 98.06%, and specificity of 99.54% in experimental experiments employing six distinct cutting-edge approaches.