Plant Disease Detection Using Machine Learning, Deep Learning, and Metaheuristics

Prof. (Dr.) Nebojsa Bacanin obtained a PhD in 2015 from the Faculty of Mathematics, University of Belgrade, Serbia (in Computer Science, average grade 10,00). He started his university career in Serbia 18 years ago at the Graduate School of Computer Science in Belgrade. He is currently a Full Professor, Vice-Rector for Scientific Research and Head of Applied Artificial Intelligence study program at Singidunum University, Belgrade, Serbia. He is involved in scientific research in the field of computer science, and his specialty includes stochastic optimization algorithms, swarm intelligence, soft-computing, optimization, and modeling, as well as artificial intelligence algorithms, swarm intelligence, machine learning, image processing, and cloud and distributed computing. He has published more than 480 scientific papers (more than 220 SCIE papers) in high-quality journals and international conferences indexed in Clarivate Analytics JCR, Scopus, WoS, IEEExplore, and other scientific databases. As member of numerous Editorial boards in cutting-edge international journals and Committee boards of international conferences, he regularly edits and perform review activities. He has also been included in the prestigious Stanford University list of the 2% best world researchers, when the whole career is considered, in the field of Artificial Intelligence. Also, according to the AD scientific index, he is currently listed as the best researcher from the Computer Science area in Serbia. His Research fields cover metaheuristics optimization, swarm intelligence, data science, artificial intelligence, and machine learning. Dr. Amit Chhabra is an Associate Professor at the Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, India. He has over 22 years of teaching and research experience. His main areas of research are: Feature Selection using Metaheuristics, Use of Artificial Intelligence in Medicine and Sentiment Analysis, Applications of Metaheuristics, and Cloud Task Scheduling. He has published a 50+ research papers in the high-quality SCI/SCIE/WoS/SCOPUS journals. He is an active reviewer of many prestigious journals which includes IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE Internet of Things, IEEE Transactions on Services Computing, IEEE transactions on consumer electronics, IEEE Transactions on Sustainable Computing, IEEE transactions on System, Man, and Cybernetics, ACM Computing Surveys, ACM transactions on embedded computing systems, Information sciences, Artificial Intelligence Review, Alexandria Engineering Journal, Journal of Ambient Intelligence and Humanized Computing, Concurrency and Computation: Practice and Experience, Cluster Computing, The Journal of Supercomputing, etc.Dr. Satveer Kour has a BTech in Information Technology from Technological Institute of Textile & Science, Bhiwani, Haryana, India (2006). She has a MTech in Computer Science and Engineering from Chaudhary Devi Lal University, Sirsa, Haryana, India in 2009. She completed her PHD from SLIET, Longowal (India) in 2022. She is currently an Assistant Professor in CET Department of Guru Nanak Dev University, Amritsar. Her research interests include Wireless Networks, Mobile Ad-Hoc Networks, Vehicular Ad-Hoc Networks, Flying Ad-Hoc Networks, Wireless Sensor Networks, Mobility Models, and Quantum Computing. She is also a life member of IAENG. She has over 10 years of work experience. She holds 4 Indian Patents. Dr. Ghaith Manita is an Associate Professor at the Faculty of Science of Tunis, University of Tunis ElManar, specializing in artificial intelligence, metaheuristics, and optimization. He has a Doctorate in Computer Science with a focus on optimizing production workshops using meta-heuristic methods from the National School of Computer Science, Tunis. Dr. Manita has an extensive publication record, with over 30 peer-reviewed articles, particularly in areas integrating operations research, logistics, and AI innovations. He teaches various subjects, from web programming to machine learning, and has supervised numerous master's and doctoral theses. His research endeavours are dedicated to linking theoretical insights with practical applications in intelligent systems and advanced computing methodologies.

• Preface. 1. Introduction to Plant Disease Detection in Agriculture. 2. Machine Learning for Agricultural Application. 3. Advancing Mango Leaf Disease Detection Through Deep Learning: YOLOv11 Versus YOLOv12. 4. Metaheuristics and Optimization in Agricultural AI: Advancing Plant Disease Detection and Smart Farming. 5. Data Acquisition and Preprocessing for Plant Disease Detection. 6. Machine Learning for Disease Classification and Prediction. 7. Deep Learning Architectures for Image-Based Plant Disease Analysis. 8. Hybrid Models: Integrating ML, DL, and Metaheuristics. 9. Stage-Wise Attention-Guided CNN (2S-XAI-CNN) for High-Accuracy Rice Leaf Disease Classification. 10. Limitations and Challenges of Artificial Intelligence in Crop Disease Detection. 11. Economic Environment Impact of AI and Machine Learning in Plant Disease Detection and Sustainable Agriculture. 12. Emerging Trends and Innovations. 13. Roadmap for the Future Research. 14. Future Prospects of Plant Disease Detection in Sustainable Agriculture.