A Spider Wasp Optimizer-Based Deep Learning Framework for Efficient Citrus Disease Detection
Main Article Content
Keywords
Citrus Disease Detection, Deep Convolutional Neural Network, Spider Wasp Optimizer, Hyperparameter Optimization
Abstract
Managing citrus diseases is important for lowering crop losses and raising the economic value of citrus output. To provide a novel approach for the identification and classification of three significant citrus diseases—Citrus Canker, Citrus Greening, and Citrus Black Spot—this study uses a Deep Convolutional Neural Network (DCNN) optimized using the Spider Wasp Optimizer (SWO). Traditional disease diagnosis methods heavily rely on expert visual inspection, which is often subjective and time-consuming. To overcome these drawbacks, the proposed SWO-DCNN model automates hyperparameter tuning, improving classification accuracy and reducing computation time. Citrus image datasets containing both healthy and infected samples were pre-processed using grayscale conversion, normalization, and augmentation, and then trained using a 10-fold cross-validation technique. Performance evaluations based on sensitivity, specificity, false positive rate, accuracy, and identification time show that the SWO-DCNN outperforms the conventional DCNN in every disease category. With accuracies of 96.22%, 96.51%, 95.70%, and 97.04% for the classification of Black Spot, Greening, Canker, and overall healthy/non-healthy, respectively, the SWO-DCNN significantly reduced false positive rates and recognition times. This paper contributes to knowledge by presenting the Spider Wasp Optimizer, a hyperparameter tuning technique for deep learning models used in the identification of agricultural diseases. The SWO-DCNN framework offers a dependable and scalable approach for automated citrus disease classification by enhancing model performance and computational efficiency. This innovation supports precision farming initiatives and provides a reliable alternative to traditional diagnostic methods, which may improve export quality control and reduce citrus farming's financial losses.
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Abisola Olayiwola, Department of Computer Engineering, Olabisi Onabanjo University, Ago Iwoye, Ogun State, Nigeria
Dr. Abisola Ayomide Olayiwola is a Lecturer in the Department of Computer Engineering at Olabisi Onabanjo University, Ogun State. She holds a Ph.D. in Intelligent Systems Engineering from Obafemi Awolowo University, Ile-Ife, Osun sate and has over a decade of experience spanning academia and international research collaborations. Her areas of expertise include Artificial Intelligence, Natural Language Processing, and Software Engineering. Dr. Olayiwola is a registered COREN engineer and an active member of the Nigerian Computer Society.
Ajibola Oyedeji, Department of Electrical Engineering Science, University of Johannesburg, Johannesburg, 2006, South Africa.
Ajibola Oyedeji is a Lecturer at the Department of Computer Engineering, Olabisi Onabanjo University, Nigeria. He received his B.Sc. and M.S degrees in Computer Engineering from the Olabisi Onabanjo University, Nigeria and Eastern Mediterranean University, TRNC, respectively. He conducts his research in artificial intelligence, computer vision, IoT and wireless sensor networks.
Dare Olayiwola, Department of Computer Engineering, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
Dr. Olayiwola Dare holds a Ph.D. in Computer Engineering from Ladoke Akintola University of Technology, Ogbomoso, Oyo state. His areas of expertise include Artificial Intelligence, Biometrics, and Software Engineering. Dr. Olayiwola Dare is a registered COREN engineer.
Olufemi Awodoye, Department of Computer Engineering, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
Engr. Dr. O. O. Awodoye is an eminent faculty member in the Department of Computer Engineering, Faculty of Engineering at Ajayi Crowther University (ACU), Oyo State, Nigeria. His areas of specialization include Biometrics, Pattern Recognition, Artificial Intelligence (AI) and Machine Learning. He is also registered by The Council Regulating Engineering in Nigeria (COREN) as a professional Engineer. He has some articles and conference publications published in various research platforms worldwide. He can be communicated via email at ooawodoye50@lautech.edu.ng.
Olukunle Oyebode, Department of Computer Science, Faculty of Computing, University of Ilesa, Osun State, Nigeria
Dr. E. O. Oyebode is a Senior Lecturer in the Department of Computer Science, Faculty of Computing, University of Ilesa, Osun State, Nigeria. His areas of specialization include Cloud Computing, Computer Networks, Cyber Security and Knowledge- based Systems. He is also a registered member of the Nigeria Computer Society. He has some articles and conference publications published in various research platforms worldwide. He can be communicated via email at ebenezer_oyebode@unilesa.edu.ng.