Abstract
Research on pertinent topics is more important than ever for the long-term development of agriculture, given the advancements in contemporary farming and use of artificial intelligence (AI) for identifying crop illnesses. There are numerous diseases, and they all significantly affect the amount and quality of potatoes. Early and automated detection of these illnesses during the budding phase can assist increase the output of potato crops, but it requires a high level of ability. Several models have already been created to identify various plant diseases. In this study, we use a variety of convolutional neural network designs to recognize potato leaf disease and assess their early detection accuracy against that of other researchers’ work. The learning sample for our algorithm included both the original and enhanced photos, as a learning option. The model was then evaluated to ensure that it was accurate. After being trained on the dataset for the potato leaf disease using the Inception-v3, Xception, and ResNet50 models, the model’s performance was evaluated using test images. ResNet50 has the highest accuracy and lowest error rate for detecting potato leaf disease, followed by Inception-v3 with an accuracy of nighty four point two five percent (94.25%) and Xception with an accuracy of eighty-nine point seven one percent (89.71%).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tiwari, D., Ashish, M., Gangwar, N., Sharma, A., Patel, S., Bhardwaj, S.: Potato leaf diseases detection using deep learning. In: 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 461–466. IEEE (2020)
Sravan, V., Swaraj, K., Meenakshi, K., Kora, P.: A deep learning based crop disease classification using transfer learning. In: Materials Today: Proceedings (2021)
Islam, F., Hoq, M.N., Rahman, C.M.: Application of transfer learning to detect potato disease from leaf image. In: 2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), pp. 127–130. IEEE (2019)
Chen, J., Zhang, D., Nanehkaran, Y.A.: Identifying plant diseases using deep transfer learning and enhanced lightweight network. Multimed. Tools Appl. 79(41–42), 31497–31515 (2020). https://doi.org/10.1007/s11042-020-09669-w
Dasgupta, S.R., Rakshit, S., Mondal, D., Kole, D.K.: Detection of diseases in potato leaves using transfer learning. In: Das, A.K., Nayak, J., Naik, B., Pati, S.K., Pelusi, D. (eds.) Computational Intelligence in Pattern Recognition. AISC, vol. 999, pp. 675–684. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-9042-5_58
Mukti, I.Z., Biswas, D.: Transfer learning based plant diseases detection using ResNet50. In: 2019 4th International Conference on Electrical Information and Communication Technology (EICT), pp. 1–6. IEEE (2019)
Too, E.C., Yujian, L., Njuki, S., Yingchun, L.: A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric. 161, 272–279 (2019)
Sumalatha, G., Krishna Rao, D.S., Singothu, D., Rani, J.: Transfer learning-based plant disease detection (2021)
Arshad, M.S., Rehman, U.A., Fraz, M.M.: Plant disease identification using transfer learning. In: 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2), pp. 1–5. IEEE (2021)
Jasim, M.A., Al-Tuwaijari, J.M.: Plant leaf diseases detection and classification using image processing and deep learning techniques. In: 2020 International Conference on Computer Science and Software Engineering (CSASE), pp. 259–265. IEEE (2020)
Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)
Mia, J., Bijoy, H.I., Uddin, S., Raza, D.M.: Real-time herb leaves localization and classification using YOLO. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–7 (2021). https://doi.org/10.1109/ICCCNT51525.2021.9579718
Krishna, R., Menzies, T.: Bellwethers: a baseline method for transfer learning. IEEE Trans. Softw. Eng. 45(11), 1081–1105 (2018)
Theckedath, D., Sedamkar, R.R.: Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Comput. Sci. 1(2), 1–7 (2020). https://doi.org/10.1007/s42979-020-0114-9
Bitto, A.K., Mahmud, I.: Multi categorical of common eye disease detect using convolutional neural network: a transfer learning approach. Bull. Electr. Eng. Inform. 11(4), 2378–2387 (2022)
François, C.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
Xia, X., Xu, C., Nan, B.: Inception-v3 for flower classification. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 783–787. IEEE (2017)
Hasan, S., Rabbi, G., Islam, R., Bijoy, H.I., Hakim, A.: Bangla font recognition using transfer learning method. In: 2022 International Conference on Inventive Computation Technologies (ICICT), pp. 57–62 (2022). https://doi.org/10.1109/ICICT54344.2022.9850765
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Bitto, A.K., Bijoy, M.I., Das, A., Rahman, M., Rabbani, M. (2023). Potato-Net: Classifying Potato Leaf Diseases Using Transfer Learning Approach. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-34619-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-34619-4_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34618-7
Online ISBN: 978-3-031-34619-4
eBook Packages: Computer ScienceComputer Science (R0)