Enhancing Cassava Leaf Disease Detection through Traditional Segmentation and Attention-driven Deep Learning Approaches
Abstract
Diseases of cassava leaves pose a significant threat to food security in tropical regions, as well as to harvests. This paper presents a combined approach for accurate and interpretable cassava leaf disease diagnosis, utilising a deep learning-based ARMUNet architecture alongside conventional image segmentation techniques. Beginning with classical methods—Otsu Thresholding, Distance Transform, and Watershed—the pipeline generates boundary-aware lesion maps that effectively isolate diseased areas. These initial segmentations guide ARMUNet, an enhanced version of UNet with attention gates and residual multi-scale encoders, allowing the model to focus on relevant lesion features while minimising background interference. Featuring various geographical and semantic elements, a Multi-Level Feature Extraction system aids in the correct classification of diseases. Detection accuracy is further improved by an ensemble method that combines ARMUNet predictions with classifiers such as PINN, ResNet50, and EfficientNetB0. The proposed system offers a scalable solution for plant disease diagnostics in precision agriculture, demonstrating high performance, interpretability, and real-time readiness.
Keywords:
Distance Transform, Ensemble learning, Multi-Level Feature Extraction, Otsu Thresholding, Segmentation, Watershed
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References
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Haq, S. I. U., Raza, A., Lan, Y., & Wang, S. (2023). Identification of Pest Attack on Corn crops using machine learning techniques. Engineering Proceedings, 56(1), 183. https://doi.org/10.3390/ASEC2023-15953
Haq, S. I. U., Tahir, M. N., & Lan, Y. (2023). Weed detection in wheat crops using image analysis and artificial intelligence (AI). Applied Sciences, 13(15), 8840. https://doi.org/10.3390/app13158840
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Lakshmi, T., Ayyappa, Y., Aruna, V., Rani, D., Kumari, P., & Praveen, S. (2024). Advanced Medicinal Plant Recognition with Convolutional Neural Networks. International Journal of Advancement in Life Sciences Research, 7(4), 87-97. https://doi.org/10.31632/ijalsr.2024.v07i04.008
Lanjewar, M. G., & Parab, J. S. (2024). CNN and transfer learning methods with augmentation for citrus leaf diseases detection using PaaS cloud on mobile. Multimedia Tools and Applications, 83(11), 31733-31758. https://doi.org/10.1007/s11042-023-16886-6
Lanjewar, M. G., Morajkar, P., & P, P. (2024). Modified transfer learning frameworks to identify potato leaf diseases. Multimedia Tools and Applications, 83(17), 50401-50423. https://doi.org/10.1007/s11042-023-17610-0
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Maryum, A., Akram, M. U., & Salam, A. A. (2021, October). Cassava leaf disease classification using deep neural networks. In 2021 IEEE 18th international conference on smart communities: improving quality of life using ICT, IoT and AI (HONET) (pp. 32-37). IEEE. https://doi.org/10.1109/HONET53078.2021.9615488
Methil, A., Agrawal, H., & Kaushik, V. (2021, July). One-vs-all methodology based cassava leaf disease detection. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. https://doi.org/10.1109/ICCCNT51525.2021.9579920
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in plant science, 7, 215232. https://doi.org/10.3389/fpls.2016.01419
Oyewola, D. O., Dada, E. G., Misra, S., & Damaševičius, R. (2021). Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing. PeerJ Computer Science, 7, e352. https://doi.org/10.7717/peerj-cs.352
Panchbhai, K. G., Lanjewar, M. G., Malik, V. V., & Charanarur, P. (2024). Small size CNN (CAS-CNN), and modified MobileNetV2 (CAS-MODMOBNET) to identify cashew nut and fruit diseases. Multimedia Tools and Applications, 83(42), 89871-89891. https://doi.org/10.1007/s11042-024-19042-w
Prashanth, J. S., Moparthi, N. R., Krishna, G. B., Prasad, A. K., Sravankumar, B., & Rao, P. R. (2024). MPCSAR-AHH: A hybrid deep learning model for real-time detection of cassava leaf diseases and fertilizer recommendation. Computers and Electrical Engineering, 119, 109628. https://doi.org/10.1016/j.compeleceng.2024.109628
Rajalakshmi, R., Shinow, A. B., Murali, A., Nair, K. S., & Bhuvana, J. (2022). An efficient convolutional neural network with Image Augmentation for Cassava Leaf Disease Detection. In Recurrent Neural Networks (pp. 289-305). CRC Press. https://doi.org/10.1201/9781003307822
Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., & Hughes, D. P. (2017). Deep learning for image-based cassava disease detection. Frontiers in plant science, 8, 1852. https://doi.org/10.3389/fpls.2017.01852
Ravi, V., Acharya, V., & Pham, T. D. (2022). Attention deep learning‐based large‐scale learning classifier for Cassava leaf disease classification. Expert Systems, 39(2), e12862. https://doi.org/10.1111/exsy.12862
S, P., K, P., Bommanaboina, Y., Meharaj, S., Tallam, V. S., & Vallabhaneni, S. C. (2025). Multi-Crop Plant Leaf Disease Detection Using Lite Models. International Journal of Advancement in Life Sciences Research, 8(2), 113-128. https://doi.org/10.31632/ijalsr.2025.v08i02.009
Sangbamrung, I., Praneetpholkrang, P., & Kanjanawattana, S. (2020). A novel automatic method for cassava disease classification using deep learning. Journal of Advances in Information Technology Vol, 11(4), 241-248. https://doi.org/10.12720/jait.11.4.241-248
Sapre, M., Jatti, V. S., Tiwari, P., Kodachakki, N., & Undale, A. (2023). Disease classification in Cassava Plant by Artificial neural network. In Machine Learning and Optimization for Engineering Design (pp. 75-84). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-7456-6_6.
Sholihin, M., Fudzee, M. F. M., & Ismail, M. N. (2023). AlexNet-Based Feature Extraction for Cassava Classification: A Machine Learning Approach. Baghdad Science Journal, 20(6 (Suppl.)), 2624-2624. https://doi.org/10.21123/bsj.2023.9120
Singh, R., Sharma, A., Sharma, N., Sharma, K., & Gupta, R. (2023, February). A deep learning-based InceptionResNet V2 model for cassava leaf disease detection. In International Conference on Emerging Trends in Expert Applications & Security (pp. 423-432). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-1946-8_38
Singh, R., Sharma, A., Sharma, N., Sharma, K., & Gupta, R. (2023, February). A deep learning-based Inception ResNet V2 model for cassava leaf disease detection. In International Conference on Emerging Trends in Expert Applications & Security (pp. 423-432). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-1946-8_38 36
Srivathsan, M. S., Jenish, S. A., Arvindhan, K., & Karthik, R. (2025). An explainable hybrid feature aggregation network with residual inception positional encoding attention and EfficientNet for cassava leaf disease classification. Scientific Reports, 15(1), 11750. https://doi.org/10.1038/s41598-025-95985-w
Tewari, A. S., & Kumari, P. (2024). Lightweight modified attention based deep learning model for cassava leaf diseases classification. Multimedia Tools and Applications, 83(20), 57983-58007. https://doi.org/10.1007/s1 1042-023-17459-3
Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161, 272-279. https://doi.org/10.1016/j.compag.2018.03.032
Vijayalata, Y., Billakanti, N., Veeravalli, K., Deepa, A., & Kota, L. (2022, February). Early detection of casava plant leaf diseases using efficientNet-B0. In 2022 IEEE Delhi Section Conference (DELCON) (pp. 1-5). IEEE. https://doi.org/10.1109/delcon54057.2022.9753210
Zhang, J., Zhang, B., Qi, C., Nyalala, I., Mecha, P., Chen, K., & Gao, J. (2024). MAIANet: Signal modulation in cassava leaf disease classification. Computers and Electronics in Agriculture, 225, 109351. https://doi.org/10.1016/j.compag.2024.109351
Zuo, Z., & Bao, Y. (2019). Remote sensing image extraction of drones for agricultural applications. Revista de la Facultad de Agronomía de la Universidad del Zulia, 36(4), 1203-1212.
Ayu, H. R., Surtono, A., & Apriyanto, D. K. (2021). Deep learning for detection cassava leaf disease. In Journal of Physics: Conference Series (Vol. 1751, No. 1, p. 012072). IOP Publishing.
Chen, Y., Xu, K., Zhou, P., Ban, X., & He, D. (2022). Improved cross entropy loss for noisy labels in vision leaf disease classification. IET Image Processing, 16(6), 1511-1519. https://doi.org/10.1049/ipr2.12402
Dey, S., Ganguli, S., & De, M. (2024). Transcriptome Analysis from leaf during non-flowering vegetative growth phase of mango (Mangifera indica L. var Fazli) from Murshidabad district of West Bengal, India. International Journal of Advancement in Life Sciences Research, 7(2), 143-162. https://doi.org/10.31632/ijalsr.2024.v07i02.012
Emmanuel, A., Mwangi, R. W., Murithi, P., Fredrick, K., & Danison, T. (2023). Classification of cassava leaf diseases using deep Gaussian transfer learning model. Engineering Reports, 5(9), e12651. https://doi.org/10.1002/eng2.12651
Emuoyibofarhe, O., Emuoyibofarhe, J. O., Adebayo, S., Ayandiji, A., Demeji, O., & James, O. (2019). Detection and classification of cassava diseases using machine learning. ir.bowen.edu.ng:8080/jspui/handle/123456789/844
H. Zhang, Y. Xu and J. Sun, "Detection of Cassava Leaf Diseases Using Self-supervised Learning," 2021 2nd International Conference on Computer Science and Management Technology (ICCSMT), Shanghai, China, 2021, pp. 120-123, https://doi.org/10.1109/ICCSMT54525.2021.00032
Haq, S. I. U., Raza, A., Lan, Y., & Wang, S. (2023). Identification of Pest Attack on Corn crops using machine learning techniques. Engineering Proceedings, 56(1), 183. https://doi.org/10.3390/ASEC2023-15953
Haq, S. I. U., Tahir, M. N., & Lan, Y. (2023). Weed detection in wheat crops using image analysis and artificial intelligence (AI). Applied Sciences, 13(15), 8840. https://doi.org/10.3390/app13158840
https://doi.org/10.1088/1742-6596/1751/1/012072
Kataoka, T., Kaneko, T., Okamoto, H., & Hata, S. (2003, July). Crop growth estimation system using machine vision. In Proceedings 2003 IEEE/ASME international conference on advanced intelligent mechatronics (AIM 2003) (Vol. 2, pp. b1079-b1083). IEEE. https://doi.org/10.1109/AIM.2003.1225492
Lakshmi, T., Ayyappa, Y., Aruna, V., Rani, D., Kumari, P., & Praveen, S. (2024). Advanced Medicinal Plant Recognition with Convolutional Neural Networks. International Journal of Advancement in Life Sciences Research, 7(4), 87-97. https://doi.org/10.31632/ijalsr.2024.v07i04.008
Lanjewar, M. G., & Parab, J. S. (2024). CNN and transfer learning methods with augmentation for citrus leaf diseases detection using PaaS cloud on mobile. Multimedia Tools and Applications, 83(11), 31733-31758. https://doi.org/10.1007/s11042-023-16886-6
Lanjewar, M. G., Morajkar, P., & P, P. (2024). Modified transfer learning frameworks to identify potato leaf diseases. Multimedia Tools and Applications, 83(17), 50401-50423. https://doi.org/10.1007/s11042-023-17610-0
Lilhore, U. K., Imoize, A. L., Lee, C. C., Simaiya, S., Pani, S. K., Goyal, N., ... & Li, C. T. (2022). Enhanced convolutional neural network model for cassava leaf disease identification and classification. Mathematics, 10(4), 580. https://doi.org/10.3390/math10040580
Maryum, A., Akram, M. U., & Salam, A. A. (2021, October). Cassava leaf disease classification using deep neural networks. In 2021 IEEE 18th international conference on smart communities: improving quality of life using ICT, IoT and AI (HONET) (pp. 32-37). IEEE. https://doi.org/10.1109/HONET53078.2021.9615488
Methil, A., Agrawal, H., & Kaushik, V. (2021, July). One-vs-all methodology based cassava leaf disease detection. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. https://doi.org/10.1109/ICCCNT51525.2021.9579920
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in plant science, 7, 215232. https://doi.org/10.3389/fpls.2016.01419
Oyewola, D. O., Dada, E. G., Misra, S., & Damaševičius, R. (2021). Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing. PeerJ Computer Science, 7, e352. https://doi.org/10.7717/peerj-cs.352
Panchbhai, K. G., Lanjewar, M. G., Malik, V. V., & Charanarur, P. (2024). Small size CNN (CAS-CNN), and modified MobileNetV2 (CAS-MODMOBNET) to identify cashew nut and fruit diseases. Multimedia Tools and Applications, 83(42), 89871-89891. https://doi.org/10.1007/s11042-024-19042-w
Prashanth, J. S., Moparthi, N. R., Krishna, G. B., Prasad, A. K., Sravankumar, B., & Rao, P. R. (2024). MPCSAR-AHH: A hybrid deep learning model for real-time detection of cassava leaf diseases and fertilizer recommendation. Computers and Electrical Engineering, 119, 109628. https://doi.org/10.1016/j.compeleceng.2024.109628
Rajalakshmi, R., Shinow, A. B., Murali, A., Nair, K. S., & Bhuvana, J. (2022). An efficient convolutional neural network with Image Augmentation for Cassava Leaf Disease Detection. In Recurrent Neural Networks (pp. 289-305). CRC Press. https://doi.org/10.1201/9781003307822
Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., & Hughes, D. P. (2017). Deep learning for image-based cassava disease detection. Frontiers in plant science, 8, 1852. https://doi.org/10.3389/fpls.2017.01852
Ravi, V., Acharya, V., & Pham, T. D. (2022). Attention deep learning‐based large‐scale learning classifier for Cassava leaf disease classification. Expert Systems, 39(2), e12862. https://doi.org/10.1111/exsy.12862
S, P., K, P., Bommanaboina, Y., Meharaj, S., Tallam, V. S., & Vallabhaneni, S. C. (2025). Multi-Crop Plant Leaf Disease Detection Using Lite Models. International Journal of Advancement in Life Sciences Research, 8(2), 113-128. https://doi.org/10.31632/ijalsr.2025.v08i02.009
Sangbamrung, I., Praneetpholkrang, P., & Kanjanawattana, S. (2020). A novel automatic method for cassava disease classification using deep learning. Journal of Advances in Information Technology Vol, 11(4), 241-248. https://doi.org/10.12720/jait.11.4.241-248
Sapre, M., Jatti, V. S., Tiwari, P., Kodachakki, N., & Undale, A. (2023). Disease classification in Cassava Plant by Artificial neural network. In Machine Learning and Optimization for Engineering Design (pp. 75-84). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-7456-6_6.
Sholihin, M., Fudzee, M. F. M., & Ismail, M. N. (2023). AlexNet-Based Feature Extraction for Cassava Classification: A Machine Learning Approach. Baghdad Science Journal, 20(6 (Suppl.)), 2624-2624. https://doi.org/10.21123/bsj.2023.9120
Singh, R., Sharma, A., Sharma, N., Sharma, K., & Gupta, R. (2023, February). A deep learning-based InceptionResNet V2 model for cassava leaf disease detection. In International Conference on Emerging Trends in Expert Applications & Security (pp. 423-432). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-1946-8_38
Singh, R., Sharma, A., Sharma, N., Sharma, K., & Gupta, R. (2023, February). A deep learning-based Inception ResNet V2 model for cassava leaf disease detection. In International Conference on Emerging Trends in Expert Applications & Security (pp. 423-432). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-1946-8_38 36
Srivathsan, M. S., Jenish, S. A., Arvindhan, K., & Karthik, R. (2025). An explainable hybrid feature aggregation network with residual inception positional encoding attention and EfficientNet for cassava leaf disease classification. Scientific Reports, 15(1), 11750. https://doi.org/10.1038/s41598-025-95985-w
Tewari, A. S., & Kumari, P. (2024). Lightweight modified attention based deep learning model for cassava leaf diseases classification. Multimedia Tools and Applications, 83(20), 57983-58007. https://doi.org/10.1007/s1 1042-023-17459-3
Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161, 272-279. https://doi.org/10.1016/j.compag.2018.03.032
Vijayalata, Y., Billakanti, N., Veeravalli, K., Deepa, A., & Kota, L. (2022, February). Early detection of casava plant leaf diseases using efficientNet-B0. In 2022 IEEE Delhi Section Conference (DELCON) (pp. 1-5). IEEE. https://doi.org/10.1109/delcon54057.2022.9753210
Zhang, J., Zhang, B., Qi, C., Nyalala, I., Mecha, P., Chen, K., & Gao, J. (2024). MAIANet: Signal modulation in cassava leaf disease classification. Computers and Electronics in Agriculture, 225, 109351. https://doi.org/10.1016/j.compag.2024.109351
Zuo, Z., & Bao, Y. (2019). Remote sensing image extraction of drones for agricultural applications. Revista de la Facultad de Agronomía de la Universidad del Zulia, 36(4), 1203-1212.
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Sirisha, U., Nakka, R., Hymavathi, J., Lalitha, S., Praveen, S., & Karras, D. (2025). Enhancing Cassava Leaf Disease Detection through Traditional Segmentation and Attention-driven Deep Learning Approaches. International Journal of Advancement in Life Sciences Research, 8(4), 138-157. https://doi.org/https://doi.org/10.31632/ijalsr.2025.v08i04.011
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