A Transfer Learning-Based Efficient Model for the Detection of Plant Leaf Diseases

Year 2025, Volume: 31 Issue: 4, 981 - 997, 30.09.2025

Zunaira Zainab Rabbia Mahum , Emad Abouel Nasr Mohammad Shehab Haseeb Hassan Mohammed El-meligy

https://doi.org/10.15832/ankutbd.1635917

Abstract

Plant disease control is necessary in agriculture since it can result in considerable crop yield losses. To reduce damage, quick diagnosis and categorization of plant leaf diseases is required; unfortunately, this process takes a lot of time and needs human efforts. To deal with these issues, a novel computerized approach for fast observation and categorization is required. There exist methodologies based on Deep Learning (DL) techniques that make use of an easily accessible dataset, namely The Plant Village Dataset. However, they may fail to recognize the diseases on unseen data due to less diverse feature extraction. Therefore, this research proposed a plant disease detector based on Deep Learning model using images of leaves and can identify several plant diseases. First, we perform image preprocessing operations. Second, Convolutional Neural Network (CNN) having several convolution and pooling layers is employed and the results are evaluated with existing DL models with varying hyper-parameters. After training, the model is carefully evaluated to validate the findings. We conducted several trials using the proposed model and attained testing accuracy of 97.6%

Keywords

Transfer Learning , CNN , Plant Disease , Agriculture

References

• Afsharpour P, Zoughi T, Deypir M & Zoqi M J (2024). Robust deep learning method for fruit decay detection and plant identification: enhancing food security and quality control. Frontiers in Plant Science, 15, 1366395.
• Albahar M (2023). A survey on deep learning and its impact on agriculture: Challenges and opportunities. Agriculture 13(3): 540
• Altieri M A (1995). Agroecology: the science of sustainable agricultura. Boulder: Westview Press Inc. 433 pp
• Anand R, Veni S & Aravinth J (2016). An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method. Paper presented at the 2016 international conference on recent trends in information technology (ICRTIT).
• Bajpai C, Sahu R & Naik K J (2023). Deep learning model for plant-leaf disease detection in precision agriculture. International Journal of Intelligent Systems Technologies and Applications 21(1): 72-91
• Biswas S, Jagyasi B, Singh B P & Lal M (2014). Severity identification of potato late blight disease from crop images captured under uncontrolled environment. Paper presented at the 2014 IEEE Canada international humanitarian technology conference-(IHTC).
• Brahimi M, Boukhalfa K & Moussaoui A (2017). Deep learning for tomato diseases: classification and symptoms visualization. Applied Artificial Intelligence 31(4): 299-315
• Brahimi M, Mahmoudi S, Boukhalfa K & Moussaoui A (2019). Deep interpretable architecture for plant diseases classification. Paper presented at the 2019 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA).
• Bruinsma J (2009). The resource outlook to 2050: by how much do land, water and crop yields need to increase by 2050? Expert meeting on how to feed the world in 2050. http://www. fao. org/wsfs/forum2050/wsfs-background-documents/wsfs-expert-papers/en/.
• Butte S, Vakanski A, Duellman K, Wang H & Mirkouei A (2021). Potato crop stress identification in aerial images using deep learning‐based object detection. Agronomy Journal 113(5): 3991-4002
• Cap Q H, Uga H, Kagiwada S & Iyatomi H (2020). Leafgan: An effective data augmentation method for practical plant disease diagnosis. IEEE Transactions on Automation Science and Engineering.
• Chao X, Sun G, Zhao H, Li M & He D (2020). Identification of apple tree leaf diseases based on deep learning models. Symmetry, 12(7): 1065
• Chollet F (2017). Xception: Deep learning with depthwise separable convolutions. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition. Demilie W B (2024). Plant disease detection and classification techniques: a comparative study of the performances. Journal of Big Data, 11(1): 5
• Erkan U, Abdurrahim T & Deniz U(2023). "Hyperparameter optimization of deep CNN classifier for plant species identification using artificial bee colony algorithm." Journal of Ambient Intelligence and Humanized Computing 14, no. 7 (2023): 8827-8838
• Fuentes A, Yoon S, Kim S C & Park D S (2019). A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition. Sensors in Agriculture: Volume 1, 17, 153.
• Haffner O, Kučera E & Rosinová D (2024). Applications of machine learning and computer vision in industry 4.0. In (Vol. 14, pp. 2431): MDPI.
• Harakannanavar S S, Rudagi J M, Puranikmath V I, Siddiqua A & Pramodhini R (2022). Plant leaf disease detection using computer vision and machine learning algorithms. Global Transitions Proceedings 3(1): 305-310
• Hasan R I, Yusuf S M & Alzubaidi L (2020). Review of the state of the art of deep learning for plant diseases: a broad analysis and discussion. Plants 9(10): 1302
• Hidayatuloh A, Nursalman M & Nugraha E (2018). Identification of tomato plant diseases by Leaf image using squeezenet model. Paper presented at the 2018 International Conference on Information Technology Systems and Innovation (ICITSI).
• Howlader M R, Habiba U, Faisal R H & Rahman M M (2019). Automatic recognition of guava leaf diseases using deep convolution neural network. Paper presented at the 2019 international conference on electrical, computer and communication engineering (ECCE).
• Huang G, Liu Z, Van Der Maaten L & Weinberger K Q (2017). Densely connected convolutional networks. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
• Huang Z, Jiang X, Huang S, Qin S & Yang S (2023). An efficient convolutional neural network-based diagnosis system for citrus fruit diseases. Frontiers in Genetics, 14, 1253934
• Hughes D & Salathé M (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060.
• Iqbal M A & Talukder K H (2020). Detection of potato disease using image segmentation and machine learning. Paper presented at the 2020 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET).
• Kalra D, Sharma S & Patel A (2023). A Review on Impact of Digital Marketing on Consumer Purchase Behaviour. Journal of Scientific Research and Technology pp. 15-20
• Katafuchi R & Tokunaga T (2020). Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors. arXiv preprint arXiv:2011.14306. Katumba A, Bomera M, Mwikirize C, Namulondo G, Ajero M G, Ramathani I, Serugunda J (2020). A Deep Learning-based Detector for Brown Spot Disease in Passion Fruit Plant Leaves. arXiv preprint arXiv:2007.14103.
• Kawasaki Y, Uga H, Kagiwada S & Iyatomi H (2015). Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. Paper presented at the International symposium on visual computing.
• Khirade S D & Patil A (2015). Plant disease detection using image processing. Paper presented at the 2015 International conference on computing communication control and automation.
• Liu B, Ding Z, Zhang Y, He D & He J (2020). Kiwifruit Leaf Disease Identification Using Improved Deep Convolutional Neural Networks. Paper presented at the 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC).
• Liu J & Wang X (2020a). Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model. Plant Methods 16(1): 1 16
• Liu J & Wang X (2020b). Tomato diseases and pests detection based on improved Yolo V3 convolutional neural network. Frontiers in plant science, 11: 898
• Lokesh G H, Chandregowda S B, Vishwanath J, Ravi V, Ravi P & Al Mazroa A (2024). Intelligent Plant Leaf Disease Detection Using Generative Adversarial Networks: a Case-study of Cassava Leaves. The Open Agriculture Journal 18(1)
• Mahum R, Munir H, Mughal Z.-U.-N., Awais M, Khan F S, Saqlain M, Mahamad S & Tlili I (2023). A novel framework for potato leaf disease detection using an efficient deep learning model. Human and Ecological Risk Assessment: An International Journal 29(2): 303–326. https://doi.org/10.1080/10807039.2022.2103753
• Mahum R, Irtaza A & Javed A (2023). EDL-Det: A robust TTS synthesis detector using VGG19-based YAMNet and ensemble learning block. IEEE Access 11: 134701–134716. https://doi.org/10.1109/ACCESS.2023.3325834
• Masazhar A N I & Kamal M M (2017). Digital image processing technique for palm oil leaf disease detection using multiclass SVM classifier. Paper presented at the 2017 IEEE 4th International conference on smart instrumentation, measurement and application (ICSIMA).
• Mondal D, Chakraborty A, Kole D K & Majumder D D (2015). Detection and classification technique of Yellow Vein Mosaic Virus disease in okra leaf images using leaf vein extraction and Naive Bayesian classifier. Paper presented at the 2015 international conference on soft computing techniques and implementations (ICSCTI).
• Pandian J A, Kumar V D, Geman O, Hnatiuc M, Arif M & Kanchanadevi K (2022). Plant disease detection using deep convolutional neural network. Applied Sciences 12(14): 6982
• Patil S, Patil D, Khawate S, Goel M A & Bhende M (2024). Detailed Plant Disease Identification Through a Neural Network and Deep Learning. Frontiers in Health Informatics 13(3): 1524-1532
• Ren S, He K, Girshick R & Sun J (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28
• Rothe P R & Rothe J P (2019). Intelligent pattern recognition system with application to cotton leaf disease identification. Paper presented at the Innovations in Computer Science and Engineering: Proceedings of the Fifth ICICSE 2017.
• Saleem M H, Potgieter J & Arif K M (2019). Plant disease detection and classification by deep learning. Plants 8(11): 468
• Seyyarer E, Ayata F, Uçkan T & Karci A (2020). "Applications and Comparison of Optimization Algorithms used in Deep Learning." Anatolian Journal of Computer Sciences 5, no. 2 (2020): 90-98
• Shah D, Trivedi V, Sheth V, Shah A & Chauhan U (2022). ResTS: Residual deep interpretable architecture for plant disease detection. Information Processing in Agriculture 9(2): 212-223
• Simonyan K & Zisserman A (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
• Singh K, Kumar S & Kaur P (2019). Support vector machine classifier based detection of fungal rust disease in Pea Plant (Pisam sativam). International Journal of Information Technology 11: 485-492
• Singh V & Misra A (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric 4(1): 41–49. In.
• Sokolova M & Lapalme G (2009). A systematic analysis of performance measures for classification tasks. Information processing & management 45(4): 427-437
• Soni P & Chahar R (2016). A segmentation improved robust PNN model for disease identification in different leaf images. Paper presented at the 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES).
• Thilagavathi K, Kavitha K, Praba R D, Arina S & Sahana R (2020). Detection of diseases in sugarcane using image processing techniques. Bioscience Biotechnology Research Communications, Special Issue (11): 109-115
• Tian Y, Zhao C, Lu S & Guo X (2011). Multiple classifier combination for recognition of wheat leaf diseases. Intelligent Automation & Soft Computing 17(5): 519-529
• Tiwari D, Ashish M, Gangwar N, Sharma A, Patel S & Bhardwaj S (2020). Potato leaf diseases detection using deep learning. Paper presented at the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS).
• Wang J, Yu L, Yang J & Dong H (2021). Dba_ssd: A novel end-to-end object detection algorithm applied to plant disease detection. Information 12(11): 474
• Zhang S, Shang Y & Wang L (2015). Plant disease recognition based on plant leaf image. J. Anim. Plant Sci 25(3): 42-45