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Advancing Crop Health: The Role of Artificial Intelligence in Disease Management
Abstract
Plant pathogens pose significant challenges to global agriculture, threatening crop yields, food security, and economic sustainability. Traditional management strategies often fall short due to the unpredictable nature of diseases and resistance to chemical treatments. The advent of artificial intelligence (AI) offers transformative solutions in this domain. This review explores the integration of AI in detecting, identifying, and managing plant pathogens with unprecedented precision. Key AI methodologies such as machine learning, deep learning, and reinforcement learning are analyzed, highlighting their role in disease diagnosis, pathogen classification, and decision support systems. The potential of precision agriculture, driven by AI tools, to optimize resource use and mitigate environmental impact is also discussed. Despite its promise, AI adoption faces challenges such as inadequate data quality, high computational requirements, and limited trust in automated systems. Emerging technologies and interdisciplinary approaches are paving the way for more effective, scalable, and sustainable plant disease management systems. This review highlights AI's transformative potential in plant pathology, offering insights into its capabilities, limitations, and the innovations required to build a resilient and sustainable agricultural future.
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References
Abbasi, M., Bernardo, M. V., Váz, P., Silva, J., & Martins, P. 2024. Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study. Information, 15(9): 574.
Abdullah, Hasan M. & Mohana, Nusrat & Khan, Bhoktear & Ahmed, Syed & Hossain, Maruf & Islam, KH & Redoy, Mahadi Hasan & Ferdush, Jannatul & Bhuiyan, Md. Abdullahil & Hossain, Md & Ahamed, Tofayel. 2023. Present and future scopes and challenges of plant pest and disease (P&D) monitoring: Remote sensing, image processing, and artificial intelligence perspectives. Remote Sensing Applications: Society and Environment, 32. 100996.
Abo-Elyousr KAM; Adel D. Al-Qurashi and Najeeb M. Almasoudi 2021. Evaluation of the synergy between Schwanniomyces vanrijiae and propolis in the control of Penicillium digitatum on lemons. Egyptian Journal of Biological Pest Control, 31:66.
Aggarwal, S., Suchithra, M., Chandramouli, N., Sarada, M., Verma, A., Vetrithangam, D., ... & Ambachew Adugna, B. 2022. Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro‐Business. Scientific Programming, (1): 1757888.
Ahmad, A., Saraswat, D., & El Gamal, A. 2023. A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Smart Agricultural Technology, 100083.
Ahmed, I. & Yadav, P. K. 2023. Plant disease detection using machine learning approaches. Expert Systems, 40: e13136.
Akintuyi, O. B. (2024). Adaptive AI in precision agriculture: a review: investigating the use of self-learning algorithms in optimizing farm operations based on real-time data. Research Journal of Multidisciplinary Studies, 7(02): 016-030.
Albattah, W., Nawaz, M., Javed, A., Masood, M., & Albahli, S. 2022. A novel deep learning method for detection and classification of plant diseases. Complex & Intelligent Systems, 8:507–524.
Al-Khasawneh, M. A., Alzahrani, A., & Alarood, A. 2023. An artificial intelligence based effective diagnosis of parkinson disease using EEG signal. In Data Analysis for Neurodegenerative Disorders (pp. 239-251). Singapore: Springer Nature Singapore.
Attri, I., Awasthi, L. K., & Sharma, T. P. 2024. Machine learning in agriculture: a review of crop management applications. Multimedia Tools and Applications 83, 12875–12915.
Attri, I., Awasthi, L. K., Sharma, T. P., & Rathee, P. 2023. A review of deep learning techniques used in agriculture. Ecological Informatics. 77: 102217.
Avanzato, R., Beritelli, F., Lombardo, A., & Ricci, C. 2023. Heart DT: Monitoring and Preventing Cardiac Pathologies Using AI and IoT Sensors. Future Internet 15: 223.
Baker, S. & Xiang, W. (2023). Artificial intelligence of things for smarter healthcare: A survey of advancements, challenges, and opportunities. IEEE Communications Surveys & Tutorials, 1261 – 1293.
Balafas, V., Karantoumanis, E., Louta, M., & Ploskas, N. 2023. Machine learning and deep learning for plant disease classification and detection. IEEE Access, 11: 114352- 114377
Balaska, V., Adamidou, Z., Vryzas, Z., & Gasteratos, A. 2023. Sustainable crop protection via robotics and artificial intelligence solutions. Machines 11, 774.
Benfenati, A., Causin, P., Oberti, R., & Stefanello, G. 2021. Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging. arXiv preprint arXiv:2112.11242.
Benfenati, A., Causin, P., Oberti, R., & Stefanello, G. 2023. Unsupervised deep learning techniques for automatic detection of plant diseases: reducing the need of manual labelling of plant images. Journal of Mathematics in Industry, 13:5.
Bhatti, M. A., Zeeshan, Z., Syam, M. S., Bhatti, U. A., Khan, A., Ghadi, Y. Y., & Afzal, T. 2024. Advanced plant disease segmentation in precision agriculture using optimal dimensionality reduction with fuzzy c-means clustering and deep learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17: 18265-18277
Bradu, P., Biswas, A., Nair, C., Sreevalsakumar, S., Patil, M., Kannampuzha, S., ... & Gopalakrishnan, A. V. 2022. Recent advances in green technology and Industrial Revolution 4.0 for a sustainable future. Environmental science and pollution research international, 30: 124488–124519.
Brandon, L. & Bommu, R. 2022. Smart Agriculture Meets Healthcare: Exploring AI-Driven Solutions for Plant Pathogen Detection and Livestock Wellness Monitoring. Unique Endeavor in Business & Social Sciences. In book: Generative Artificial Intelligence in Agriculture, Education, and Business (pp.218-272) Chapter: 6 Publisher: Deep Science Publishing.
Canay, Özkan & Kocabıçak, Ümit. 2024. Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework. Knowledge-Based Systems. 306. 112710.
Castellanos, L. R., Chaffee, R., Kumar, H., Mezgebo, B. K., Kassau, P., Peirano, G., ... & Pillai, D. R. 2024. A novel machine-learning aided platform for rapid detection of urine ESBLs and carbapenemases: URECA-LAMP. Journal of Clinical Microbiology, 62(11): e00869-24.
Corceiro, A., Alibabaei, K., Assunção, E., Gaspar, P. D., & Pereira, N. 2023. Methods for detecting and classifying weeds, diseases and fruits using AI to improve the sustainability of agricultural crops: a review. Processes, 11(4): 1263.
Coulibaly, S., Kamsu-Foguem, B., Kamissoko, D., & Traore, D. 2022. Deep learning for precision agriculture: A bibliometric analysis. Intelligent Systems with Applications, 16: 200102.
Crandall, S. G., Gold, K. M., Jiménez-Gasco, M. D. M., Filgueiras, C. C., & Willett, D. S. 2020. A multi-omics approach to solving problems in plant disease ecology. PLoS One, 15(9): e0237975.
Danda, R. R. & Dileep, V. 2024. Leveraging AI and Machine Learning for Enhanced Preventive Care and Chronic Disease Management in Health Insurance Plans. Frontiers in Health Informatics, 13 (3): 6878-6891
Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. 2022. Smart farming: Internet of Things (IoT)-based sustainable agriculture. Agriculture, 12(10): 1745.
Domingues, T., Brandão, T., Ferreira, J.C., 2022. Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey. Agriculture, 12: 1350.
Elbasi, E. Zaki, C. Topcu, A.E. Abdelbaki, W. Zreikat, A.I. Cina, E.; Shdefat, A.; Saker, L. 2023. Crop Prediction Model Using Machine Learning Algorithms. Appl. Sci., 13, 9288.
Faisal, A., Al-Nahhal, I., Dobre, O. A., Ngatched, T. M., & Shin, H. 2024. Deep Reinforcement Learning for RIS-Aided Full-Duplex Systems: Advances and Challenges. IEEE Communications Magazine.
Fones, H. N., Bebber, D. P., Chaloner, T. M., Kay, W. T., Steinberg, G., & Gurr, S. J. 2020. Threats to global food security from emerging fungal and oomycete crop pathogens. Nature Food, 1(6): 332-342.
Fuentes-Peñailillo, F., Gutter, K., Vega, R., & Silva, G. C. 2024. Transformative technologies in digital agriculture: Leveraging Internet of Things, remote sensing, and artificial intelligence for smart crop management. Journal of Sensor and Actuator Networks, 13(4): 39.
García-Vera, Y. E., Polochè-Arango, A., Mendivelso-Fajardo, C. A., & Gutiérrez-Bernal, F. J. 2024. Hyperspectral image analysis and machine learning techniques for crop disease detection and identification: A review. Sustainability, 16(14): 6064.
Gautron, R., Maillard, O. A., Preux, P., Corbeels, M., & Sabbadin, R. 2022. Reinforcement learning for crop management support: Review, prospects and challenges. Computers and Electronics in Agriculture, 200: 107182.
Gil, Y., Garijo, D., Khider, D., Knoblock, C. A., Ratnakar, V., Osorio, M., ... & Shu, L. 2021. Artificial intelligence for modeling complex systems: taming the complexity of expert models to improve decision making. ACM Transactions on Interactive Intelligent Systems, 11(2): 1-49.
Goel, L. & Nagpal, J. 2023. A systematic review of recent machine learning techniques for plant disease identification and classification. IETE Technical Review. 40(2):1-17.
González-Rodríguez, V. E., Izquierdo-Bueno, I., Cantoral, J. M., Carbú, M., & Garrido, C. 2024. Artificial intelligence: A promising tool for application in phytopathology. Horticulturae, 10(3): 197.
González-Rodríguez, V. E., Izquierdo-Bueno, I., Cantoral, J. M., Carbú, M., & Garrido, C. 2024. Artificial Intelligence: A Promising Tool for Application in Phytopathology. Horticulturae, 2024: 10, 197.
Goodswen, S. J., Barratt, J. L., Kennedy, P. J., Kaufer, A., Calarco, L., & Ellis, J. T. 2021. Machine learning and applications in microbiology. FEMS microbiology reviews, 45(5): fuab015.
Goyal, S., & Singh, R. 2023. Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques. Journal of Ambient Intelligence and Humanized Computing, 14(4): 3239-3259.
Green, E. D., Gunter, C., Biesecker, L. G., Di Francesco, V., Easter, C. L., Feingold, E. A., ... & Manolio, T. A. 2020. Strategic vision for improving human health at The Forefront of Genomics. Nature, 586(7831):683-692.
Gul, D., & Banday, R. U. Z. 2024. Transforming crop management through advanced AI and machine learning: Insights into innovative strategies for sustainable agriculture. AI, Computer Science and Robotics Technology. IntechOpen. doi: 10.5772/acrt.20240030
Gupta, N., Khosravy, M., Patel, N., Dey, N., Gupta, S., Darbari, H., & Crespo, R. G. 2020. Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines. Applied Intelligence, 50(11): 3990-4016.
Han, H., Liu, Z., Li, J., & Zeng, Z. 2024. Challenges in remote sensing-based climate and crop monitoring: navigating the complexities using AI. Journal of Cloud Computing, 13:34.
Ibrahim, I., & Abdulazeez, A. 2021. The role of machine learning algorithms for diagnosing diseases. Journal of Applied Science and Technology Trends, 2(01): 10-19.
Jackulin, C. & Murugavalli, S. 2022. A comprehensive review on detection of plant disease using machine learning and deep learning approaches. Measurement: Sensors 24: 100441.
Jafar, A., Bibi, N., Naqvi, R. A., Sadeghi-Niaraki, A., & Jeong, D. 2024. Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations. Frontiers in Plant Science, 15: 1356260.
Javaid, M., Haleem, A., Khan, I. H., & Suman, R. 2023. Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem. 2(1): 15-30.
Javaid, M., Haleem, A., Singh, R. P., & Suman, R. 2022. Enhancing smart farming through the applications of Agriculture 4.0 technologies. International Journal of Intelligent Networks, 3: 150-164.
Javidan, S. M., Banakar, A., Vakilian, K. A., & Ampatzidis, Y. 2023. Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning. Smart Agricultural Technology, 3: 100081.
Jeger, M., Beresford, R., Berlin, A., Bock, C., Fox, A., Gold, K. M., & Xu, X. 2024. Impact of novel methods and research approaches in plant pathology: Are individual advances sufficient to meet the wider challenges of disease management?. Plant Pathology, 73(7): 1629-1655.
Jeger, M., Beresford, R., Bock, C., Brown, N., Fox, A., Newton, A., & Yuen, J. 2021. Global challenges facing plant pathology: multidisciplinary approaches to meet food security and environmental challenges in the mid-twenty-first century. CABI Agriculture and Bioscience, 2(1): 1-18.
Ji, D., Zhu, L., Gao, S., Xu, P., Lu, H., Ye, J., & Zhao, F. 2024. Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding. arXiv preprint arXiv:2411.08516.
Khan, N., Ray, R. L., Sargani, G. R., Ihtisham, M., Khayyam, M., & Ismail, S. 2021. Current progress and future prospects of agriculture technology: Gateway to sustainable agriculture. Sustainability, 13(9): 4883.
Khattak, A., Asghar, M. U., Batool, U., Asghar, M. Z., Ullah, H., Al-Rakhami, M., & Gumaei, A. 2021. Automatic detection of citrus fruit and leaves diseases using deep neural network model. IEEE access, 9: 112942-112954.
Kumar, M., Kumar, A., & Palaparthy, V. S. 2020. Soil sensors-based prediction system for plant diseases using exploratory data analysis and machine learning. IEEE Sensors Journal.. 21 16: 17455-17468.
Kumar, R., Chug, A., Singh, A. P., & Singh, D. 2022. A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review. Journal of Sensors. 3287561.
Kundu, N., Rani, G., Dhaka, V. S., Gupta, K., Nayaka, S. C., Vocaturo, E., & Zumpano, E. 2022. Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning. Artificial intelligence in agriculture, 6: 276-291.
Lakshmi, V., & Corbett, J. 2023. Using AI to improve sustainable agricultural practices: A literature review and research agenda. Communications of the Association for Information Systems, 53(1): 96-137.
Li, C. & Wang, M. 2024. Pest and disease management in agricultural production with artificial intelligence: Innovative applications and development trends. Advances in Resources Research, 4 (3): 381-401.
Li, J., Hui, B., Qu, G., Yang, J., Li, B., Li, B., ... & Li, Y. 2024. Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls. Advances in Neural Information Processing Systems, 36.
Li, L., Zhang, S., & Wang, B. 2021. Plant disease detection and classification by deep learning—a review. IEEE Access, 9: 56683- 56698
Liu, Z., Bashir, R. N., Iqbal, S., Shahid, M. M. A., Tausif, M., & Umer, Q. 2022. Internet of Things (IoT) and machine learning model of plant disease prediction–blister blight for tea plant. Ieee Access, 10: 44934-44944.
Madeira, M., Porfírio, R. P., Santos, P. A., & Madeira, R. N. (2024). AI-Powered Solution for Plant Disease Detection in Viticulture. Procedia Computer Science, 238, 468-475.
Mahlein, A. K., Arnal Barbedo, J. G., Chiang, K. S., Del Ponte, E. M., & Bock, C. H. 2024. From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management. Phytopathology, 114(8): 1733-1741.
Mamdouh Farghaly, H., Shams, M. Y., & Abd El-Hafeez, T. 2023. Hepatitis C Virus prediction based on machine learning framework: a real-world case study in Egypt. Knowledge and Information Systems, 65(6): 2595-2617.
Mehmood, N., Saeed, M., Zafarullah, S., Hyder, S., Rizvi, Z. F., Gondal, A. S., ... & Kupe, M. 2023. Multifaceted impacts of plant-beneficial pseudomonas spp. in managing various plant diseases and crop yield improvement. ACS omega, 8(25): 22296-22315.
Minn, S. 2022. AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence. 3: 100050.
Mohamed I. Elsayed, Adel D. Al-Qurashi, Najeeb Marei Almasaudi and, Abo-Elyousr KAM 2022. Efficacy of some essential oils against gray mold of table grapes and their effect on fruit quality. South African Journal of Botany 146 481_490.
Mondejar, M. E., Avtar, R., Diaz, H. L. B., Dubey, R. K., Esteban, J., Gómez-Morales, A., & Garcia-Segura, S. 2021. Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet. Science of The Total Environment, 794: 148539.
Monteiro, A., Santos, S., & Gonçalves, P. 2021. Precision agriculture for crop and livestock farming—Brief review. Animals, 11: 2345.
Murugan, J., & Kaliyanandi, M. 2024. Revolutionizing Precision Agriculture Using Artificial Intelligence and Machine Learning. In Data Science for Agricultural Innovation and Productivity (pp. 110-126). Bentham Science Publishers.
Nižetić, S., Šolić, P., Gonzalez-De, D. L. D. I., & Patrono, L. 2020. Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future. Journal of cleaner production, 274: 122877.
Omboni, S., McManus, R. J., Bosworth, H. B., Chappell, L. C., Green, B. B., Kario, K., ... & Wakefield, B. J. 2020. Evidence and recommendations on the use of telemedicine for the management of arterial hypertension: an international expert position paper. Hypertension, 76(5): 1368-1383.
Orchi, H., Sadik, M., & Khaldoun, M. 2021. On using artificial intelligence and the internet of things for crop disease detection: A contemporary survey. Agriculture 2022, 12: 9.
Panchal, A. V., Patel, S. C., Bagyalakshmi, K., Kumar, P., Khan, I. R., & Soni, M. 2023. Image-based plant diseases detection using deep learning. Materials Today: Proceedings, 80: 3500-3506.
Pandit, M. A., Kumar, J., Gulati, S., Bhandari, N., Mehta, P., Katyal, R., & Kaur, J. 2022. Major biological control strategies for plant pathogens. Pathogens, 11: 273.
Panigrahi, K. P., Das, H., Sahoo, A. K., & Moharana, S. C. 2020. Maize leaf disease detection and classification using machine learning algorithms. In Progress in Computing, Analytics and Networking: Proceedings of ICCAN 2019 (pp. 659-669). Springer Singapore.
Qazi, Sameer & Khawaja, Bilal & Farooq, Qazi. 2022. IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends. IEEE Access, 10. 1-1. 10.1109/ACCESS.2022.3152544.
Rahman, M. H. U., Sikder, R., Tripathi, M., Zahan, M., Ye, T., Gnimpieba Z, E., ... & Gadhamshetty, V. 2024. Machine learning-assisted raman spectroscopy and SERS for bacterial pathogen detection: clinical, food safety, and environmental applications. Chemosensors, 12(7): 140.
Rana, M. & Bhushan, M. 2023. Machine learning and deep learning approach for medical image analysis: diagnosis to detection. Multimedia Tools and Applications, 82:26731–26769.
Rane, J., Kaya, O., Mallick, S. K., & Rane, N. L. 2024. Smart farming using artificial intelligence, machine learning, deep learning, and ChatGPT: Applications, opportunities, challenges, and future directions. Generative Artificial Intelligence in Agriculture, Education, and Business, 218-272.
Ranjan, R., Sahana, B. C., & Bhandari, A. K. 2024. Deep learning models for diagnosis of schizophrenia using EEG signals: emerging trends, challenges, and prospects. Archives of Computational Methods in Engineering, 31(4): 2345-2384.
Rejeb, A., Rejeb, K., Zailani, S., Keogh, J. G., & Appolloni, A. 2022. Examining the interplay between artificial intelligence and the agri-food industry. Artificial intelligence in agriculture, 6: 111-128.
Reshi, A. A., Rustam, F., Mehmood, A., Alhossan, A., Alrabiah, Z., Ahmad, A., ... & Choi, G. S. 2021. An efficient CNN model for COVID‐19 disease detection based on x‐ray image classification. Complexity, (1): 6621607.
Richard, B., Qi, A., & Fitt, B. D. L. 2022. Control of crop diseases through Integrated Crop Management to deliver climate‐smart farming systems for low‐and high‐input crop production. Plant Pathology, 71:187–206.
Ristaino, J. B., Anderson, P. K., Bebber, D. P., Brauman, K. A., Cunniffe, N. J., Fedoroff, N. V., & Wei, Q. 2021. The persistent threat of emerging plant disease pandemics to global food security. Proceedings of the National Academy of Sciences, 118(23): e2022239118.
Rizzo, D. M., Lichtveld, M., Mazet, J. A., Togami, E., & Miller, S. A. 2021. Plant health and its effects on food safety and security in a One Health framework: four case studies. One Health Outlook 3: 6.
Rubio, L., Galipienso, L., & Ferriol, I. 2020. Detection of plant viruses and disease management: Relevance of genetic diversity and evolution. Frontiers in plant science. 11:1092.
Sánchez, J. M., Rodríguez, J. P., & Espitia, H. E. 2020. Review of artificial intelligence applied in decision-making processes in agricultural public policy. Processes, 8, 1374. doi:10.3390/pr8111374
Sandholtz, S. H., Valdes, C., Mulakken, N., Torres, M., Avila-Herrera, A., Drocco, J., ... & Be, N. 2023. PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection (No. LLNL-TR-857783). Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States).
Santos-Briones, C. D. L., Carreón-Anguiano, K. G., Vila-Luna, S. E., Todd, J. N. A., Islas-Flores, I., Sáenz-Carbonell, L., & Canto-Canché, B. 2024. Algorithms for Effector Prediction in Plant Pathogens and Pests: Achievements and Current Challenges. Microbiology Research, 15(4): 2162-2183.
Sekar, J., Aruchamy, P., Sulaima Lebbe Abdul, H., Mohammed, A. S., & Khamuruddeen, S. 2022. An efficient clinical support system for heart disease prediction using TANFIS classifier. Computational Intelligence, 38(2): 610-640.
Shahi, T. B., Xu, C. Y., Neupane, A., & Guo, W. 2023. Recent advances in crop disease detection using UAV and deep learning techniques. Remote Sens, 15: 2450.
Shaikh, T. A., Rasool, T., & Lone, F. R. 2022. Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture. 198 (C).
Sharma, K. & Shivandu, S. K. 2024. Integrating artificial intelligence and Internet of Things (IoT) for enhanced crop monitoring and management in precision agriculture. Sensors International.
Sharma, P., Berwal, Y. P. S., & Ghai, W. 2020. Performance analysis of deep learning CNN models for disease detection in plants using image segmentation. Information Processing in Agriculture. 7(4): 566-574.
Shetty, B., Fernandes, R., Rodrigues, A. P., Chengoden, R., Bhattacharya, S., & Lakshmanna, K. 2022. Skin lesion classification of dermoscopic images using machine learning and convolutional neural network. Scientific Reports, 12(1): 18134.
Shoaib, M., Shah, B., Ei-Sappagh, S., Ali, A., Ullah, A., Alenezi, F., & Ali, F. 2023. An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in Plant Science, 14: 1158933.
Siddiqui, H.U.R.; Saleem, A.A.; Raza, M.A.; Villar, S.G.; Lopez, L.A.D.; Diez, I.d.l.T; Rustam, F.; Dudley, S. 2023. Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence. Diagnostics, 13: 2881.
Singh, R. K., Berkvens, R., & Weyn, M. 2021. AgriFusion: An architecture for IoT and emerging technologies based on a precision agriculture survey. IEEE Access. 9: 136253-136283, 2021.
Singh, V., Sharma, N., & Singh, S. 2020. A review of imaging techniques for plant disease detection. Artificial Intelligence in Agriculture, 4: 229-242.
Sparrow, R., Howard, M., & Degeling, C. (2021). Managing the risks of artificial intelligence in agriculture. NJAS: Impact in Agricultural and Life Sciences, 93(1), 172-196.
Srivastava, S., Wang, W., Zhou, W., Jin, M., & Vikesland, P. J. 2024. Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review. Environmental Science & Technology, 58: 47: 20830–20848.
SS, V. C., Hareendran, A., & Albaaji, G. F. 2024. Precision farming for sustainability: An agricultural intelligence model. Computers and Electronics in Agriculture, 226: 109386.
Sujatha, R., Chatterjee, J. M., Jhanjhi, N. Z., & Brohi, S. N. 2021. Performance of deep learning vs machine learning in plant leaf disease detection. Microprocessors and Microsystems, 80: 103615.
Sun, R., Arik, S. Ö., Muzio, A., Miculicich, L., Gundabathula, S., Yin, P., ... & Pfister, T. 2023. SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended). arXiv preprint arXiv:2306.00739.
Tang, B. H., Yao, B. F., Zhang, W., Zhang, X. F., Fu, S. M., Hao, G. X., & Zhao, W. 2024. Optimal use of β-lactams in neonates: machine learning-based clinical decision support system. EBioMedicine, 105.
Tchito Tchapga, C., Mih, T. A., Tchagna Kouanou, A., Fozin Fonzin, T., Kuetche Fogang, P., Mezatio, B. A., & Tchiotsop, D. 2021. Biomedical image classification in a big data architecture using machine learning algorithms. Journal of Healthcare Engineering, (1): 9998819.
Thakur, P. S., Khanna, P., Sheorey, T., & Ojha, A. 2022. Trends in vision-based machine learning techniques for plant disease identification: A systematic review. Expert Systems with Applications, 208: 118117.
Thakur, P. S., Sheorey, T., & Ojha, A. 2023. VGG-ICNN: A Lightweight CNN model for crop disease identification. Multimedia Tools and Applications. Multimed Tools Appl 82: 497–520.
Tugrul, B., Elfatimi, E., & Eryigit, R. 2022. Convolutional neural networks in detection of plant leaf diseases: A review. Agriculture. 12: 1192.
Wani, J. A., Sharma, S., Muzamil, M., Ahmed, S., Sharma, S., & Singh, S. 2022. Machine learning and deep learning based computational techniques in automatic agricultural diseases detection: Methodologies, applications, and challenges. Archives of Computational methods in Engineering, 29(1): 641-677.
Yan, J. & Wang, X. 2022. Unsupervised and semi‐supervised learning: The next frontier in machine learning for plant systems biology. The Plant Journal ,111 (6): 1527-1538.
Yu, Y., Cao, Y., Hou, D., Disse, M., Brieden, A., Zhang, H., & Yu, R. 2022. The study of artificial intelligence for predicting land use changes in an arid ecosystem. Journal of Geographical Sciences, 32(4): 717-734.
Zamani, A. S., Anand, L., Rane, K. P., Prabhu, P., Buttar, A. M., Pallathadka, H., ... & Dugbakie, B. N. 2022. [Retracted] Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection. Journal of Food Quality, 2022(1): 1598796.
Zhang, Y., Khanduri, P., Tsaknakis, I., Yao, Y., Hong, M., & Liu, S. 2024. An Introduction to Bilevel Optimization: Foundations and applications in signal processing and machine learning. IEEE Signal Processing Magazine, 41(1): 38-59.
DOI: 10.33687/10.33687/phytopath.013.03.5526
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