ENHANCING POTATO CROP HEALTH MONITORING USING TWO STAGE CNN BASED DISEASE CLASSIFICATION MODELS
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Authors
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Aditya A. Shastri
Pandit Deendayal Energy University
https://orcid.org/0000-0001-5025-7750
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Astha More
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Tisha Soni
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Milind Ratnaparkhe
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Sashi Rawat
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Ketan Sabale
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Manish Paliwal
Keywords:
Potato disease, CNN, Deep Learning, Late blight, Early blightAbstract
Potato is one of the most extensively cultivated crops in India as well as worldwide and
serves as a staple food in many regions. Due to its agricultural and economic importance, effective
disease management is essential to ensure healthy crop yields. Traditional methods of disease detection
rely on manual visual inspection by farmers or agricultural experts, which often lack precision and are
prone to misdiagnosis, leading to substantial crop losses. This study proposes a deep learning-based
approach for automated disease detection in potato plants using a Convolutional Neural Network
(CNN). The novelty of this work lies in developing a two-stage CNN framework that first classifies
the potato leaf images as either healthy or unhealthy. Next, the unhealthy leaf images are further
classified as either early blight or late blight diseases. The proposed CNN models are optimized using
the Adam optimizer with a learning rate of 0.0001. Extensive experimentation on a dataset of 1,500
images demonstrates high classification accuracies of 98.3% and 99% for the two stages, respectively.
The results demonstrate that our proposed CNN models are highly effective for automated disease
detection and improve decision-making in potato crops.
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References
Adhikari, S. (2024). Advancements in Agricultural
Technology: Vision Transformer-Based Potato Leaf
Disease Classification. Journal of Soft Computing
Paradigm, 6(2), 169-185.
Bajpai, A., Sahu, S., & Tiwari, N. K. (2025). Integrating
Attention Mechanisms and Squeeze-and-Excitation
Blocks for Accurate Potato Leaf Disease Detection.
Potato Research, 1-21.
Chang, C. Y., & Lai, C. C. (2024). Potato leaf disease
detection based on a lightweight deep learning
model. Machine Learning and Knowledge Extraction,
6(4), 2321-2335.
Chollet, F. (2017). Xception: Deep learning with
depthwise separable convolutions. IEEE Conference
on Computer Vision and Pattern Recognition (CVPR)
(pp. 1251-1258). IEEE.
Chowdhury, N., Sultana, J., Rahman, T., Chowdhury,
T., Khan, F. T., & Chakraborty, A. (2024). Potato
leaf disease detection through ensemble average
deep learning model and classifying the disease
severity. Indonesian Journal of Electrical Engineering
and Computer Science, 35(1), 494-502.
Dalal, N., & Triggs, B. (2005). Histograms of oriented
gradients for human detection. 2005 IEEE computer
society conference on computer vision and pattern
recognition (CVPR’05). 1, pp. 886--893. IEEE.
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei,
L. (2009). ImageNet: A large-scale hierarchical image
database. IEEE Conference on Computer Vision and
Pattern Recognition (pp. 248--255). IEEE.
Fuadi, I., Putri, R. N., Nasien, D., & Oktarina, D. (2024).
Deep Learning Approaches for Potato Leaf Disease
Detection: Evaluating the Efficacy of Convolutional
Neural Network Architectures. Revue d’Intelligence
Artificielle, 38(2), 717.
Gülmez, B. (2024). A comprehensive review of
convolutional neural networks based disease
detection strategies in potato agriculture. Potato
Research, 1-35.
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep
learning in agriculture: A survey. Computers and
Electronics in Agriculture, 147, 70--90.
Lanjewar, M. G., Morajkar, P., & Payaswini, P. (2024).
Modified transfer learning frameworks to identify
potato leaf diseases. Multimedia Tools and Applications,
83(17), 50401-50423.
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016).
Using deep learning for image-based plant disease
detection. Frontiers in plant science, 7, 215232.
Muhammad, A. P. (2025, March 15). Potato Leaf Disease
Dataset. Retrieved from Kaggle: https://www.
kaggle.com/datasets/muhammadardiputra/potatoleaf-
disease-dataset
Paul, H., Ghatak, S., Chakraborty, S., Pandey, S. K.,
Dey, L., Show, D., & Maity, S. (2024). A study and
comparison of deep learning based potato leaf
disease detection and classification techniques using
explainable AI. Multimedia Tools and Applications,
83(14), 42485-42518.
Pujari, J. D., Yakkundimath, R., & Byadgi, A. S.
(2015). Image processing based detection of fungal
diseases in plants. Procedia Computer Science, 46,
1802--1808.
Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S.
M. (2024). Potato Leaf Disease Classification Using
Optimized Machine Learning Models and Feature
Selection Techniques. Potato Research, 1-25.
Rashid, J., Khan, I., Ali, G., Almotiri, S. H., AlGhamdi, M.
A., & Masood, K. (2021). Multi-level deep learning
model for potato leaf disease recognition. Electronics,
10(17), 2064.
Raza, A., Pitafi, A. H., Shaikh, M. K., & Ahmed, K.
(2025). Optimizing Potato Leaf Disease Recognition:
Insights DENSE-NET-121 and Gaussian Elimination
Filter Fusion. Heliyon, 11(3), e42318.
Reis, H. C., & Turk, V. (2024). Potato leaf disease
detection with a novel deep learning model based
on depthwise separable convolution and transformer
networks. Engineering Applications of Artificial
Intelligence, 133, 108307.
Shastri, A. A., Ahuja, K., Ratnaparkhe, M., &
Busnel, Y. (2021). Probabilistically sampled and
spectrally clustered plant species using phenotypic
characteristics. PeerJ, 9, e11927.
Shastri, A. A., Ahuja, K., Ratnaparkhe, M., Shah, A.,
Gagrani, A., & Lal, A. (2019). Vector quantized
spectral clustering applied to whole genome
sequences of plants. Evolutionary Bioinformatics, 15,
1--7.
Shastri, A. A., Prajapati, Y., Katariya, H., Paliwal, M.,
& Sabale, K. (2025). Enhancing Clinical Outcomes
Using Deep Learning Solution for Accurate Lung
Cancer Classification. Sensing and Imaging, 26(1), 19.
Shastri, A. A., Tamrakar, D., & Ahuja, K. (2018). Densitywise
two stage mammogram classification using
texture exploiting descriptors. Expert Systems with
Applications, 99, 71--82.
Singh, V., & Misra, A. K. (2017). Detection of plant
leaf diseases using image segmentation and soft
computing techniques. Information processing in
Agriculture, 4(1), 41--49.
Sofuoğlu, C. İ., & Bırant, D. (2024). Potato plant leaf
disease detection using deep learning method.
Journal of Agricultural Sciences, 30(1), 153-165.
Strange, R. N., & Scott, P. R. (2005). Plant disease:
A threat to global food security. Anual Report of
Phytopathology, 43, 83-116.
Upadhyay, S., Jain, J., & Prasad, R. (2024). Early Blight
and Late Blight Disease Detection in Potato Using
Efficientnetb0. International Journal of Experimental
Research and Review, 38, 15-25.
Wahabzada, M., Mahlein, A. K., Bauckhage, C., Steiner,
U., Oerke, E. C., & Kersting, K. (2015). Plant disease
detection from image data using deep learning. AI
and Society, 30(3), 463--474.
Zhang, C., Wang, S., Wang, C., Wang, H., Du, Y.,
& Zong, Z. (2025). Research on a Potato Leaf
Disease Diagnosis System Based on Deep Learning.
Agriculture, 15(4), 424.
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