Abstract
In the agricultural domain, sugarcane crops, like many others, are susceptible to diseases, posing a significant threat to both quality and quantity of production. Identifying and mitigating these diseases in their early stages are critical to averting financial losses for farmers. In response, researchers have turned to Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL) to analyze diverse agricultural data, including yield prediction, climate patterns, and soil quality, with disease prevention being a prime focus. This paper presents a thorough exploration of the effectiveness of a Deep Learning-based Convolutional Neural Network (CNN) algorithm tailored for the detection of prevalent sugarcane diseases in India. Motivated by the rapid evolution of disease classes and farmers’ limited diagnostic skills, this study employs advanced deep learning and computer vision techniques. Through image categorization into healthy and diseased groups, the trained model achieves an impressive 98.69% accuracy rate in sugarcane disease detection. Furthermore, to empower farmers, a web-based application is developed for ongoing disease monitoring. The paper suggests future research avenues, including user feedback integration and exploring the intersection of disease detection with agricultural productivity enhancement and price forecasting, thus enriching farmers’ decision-making processes.
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References
Park, H., Eun, J.S., Kim, S.H.: Image-based disease diagnosing and predicting of the crops through the deep learning mechanism. In: International Conference on Information and Communication Technology Convergence (ICTC), IEEE, 18–20, pp. 129–131 (2017). https://doi.org/10.1109/ICTC.2017.8190957
Dandawate, Y., Kokare, R.: An automated approach for classification of plant diseases towards the development of futuristic decision support system in Indian perspective. International Conference on Advances in Computing, https://doi.org/10.1109/ICACSIS.2018.8618169
Militante, S.V., Gerardo, B.D., Dionisio, N.V.: Plant leaf detection and disease recognition using deep learning. In: 2019 IEEE Eurasia conference on IOT, communication and engineering (ECICE), pp. 579–582. IEEE (2019)
Hu, G., Yang, X., Zhang, Y., Wan, M.: Identification of tea leaf diseases by using an improved deep convolutional neural network. Sustainable Computing: Informatics and Systems 24, 100353 (2019)
Suryawati, E., Sustika, R., Yuwana, R.S., Subekti, A., Pardede, H.F.: Deep structured convolutional neural network for tomato diseases detection. In: 2018 international conference on advanced computer science and information systems (ICACSIS), pp. 385–390. IEEE (2018)
Militante, S., Gerardo, B.: Detecting sugarcane diseases through adaptive deep learning models of convolutional neural network. In: 6th IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS), pp. 1–5. Kuala Lumpur, Malaysia (2019). https://doi.org/10.1109/ICETAS48360.2019.9117332
Huang, W., Lamb, D.W., Niu, Z., Zhang, Y., Liu, L., Wang, J.: Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyper spectral imaging. Precision Agric. 8(4/5), 187–197 (2007). https://doi.org/10.1007/s11119-007-9038-9
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791
Hou, J., Li, L., He, J.: Detection of grapevine leafroll disease based on 11-index imagery and ant colony clustering algorithm. Precision Agriculture, 1–18 (2016). https://doi.org/10.1007/s1119-0169432-2
Shruti, U., Nagaveni, V., Raghvendra, B.K.: A review on machine learning classification techniques for plant disease detection. In: 5th International Conference on Advanced Computing & Communication Systems (ICACCS), pp. 281–284. Coimbatore India (2019). https://doi.org/10.1109/ICACCS.2019.8728415
Hu, G., Wei, K., Zhang, Y., Bao, W., Liang, D.: Estimation of tea leaf blight severity in natural scene images. Precision Agriculture (Springer), 1–24 (2021). https://doi.org/10.1007/s11119-020-09782-8
Ozguvena, M.M., Adem, K.: Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Physica A: Statistical Mechanics and its Applications 535, 122537 (2019). https://doi.org/10.1016/j.physa.2019.122537
Oppenheim, D., Shani, G., Erlich, O., Tsror, L.: Using deep learning for image-based potato tuber disease detection. Phytopathology 109(6), 1083–1087 (2019)
Cruz, A.C., Luvisi, A., De Bellis, L., Ampatzidis, Y.: Vision-based plant disease detection system using transfer and deep learning. In: 2017 asabe annual international meeting, p. 1. American Society of Agricultural and Biological Engineers (2017)
Chen, J., Chen, J., Zhang, D., Sun, Y., Nanehkaran, Y.A.: Using deep transfer learning for image-based plant disease identification. Comput. Electron. Agric. 173, 105393 (2020)
Huang, S., Liu, W., Qi, F., Yang, K.: Development and validation of a deep learning algorithm for the recognition of plant disease. In: 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 1951–1957. IEEE (2019)
Nagasubramanian, K., et al.: Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods 1–10 (2019). https://doi.org/10.1186/s13007-019-0479-8
Arsenovic, M., Karanovic, M., Sladojevic, S., Anderla, A., Stefanovic, D.: Solving current limitations of deep learning-based approaches for plant disease detection. Symmetry 11, 1–21 (2019). https://doi.org/10.3390/sym11070939
Barbedo, J.: Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Computer and Electronics in Agriculture 153 (2018). https://doi.org/10.1016/j.compag.2018.08.03
Prabavathi, R., Chelliah, B.J.: A comprehensive review on machine learning approaches for yield prediction using essential soil nutrients. Universal Journal of Agricultural Research 10(3), 288–303 (2022). https://doi.org/10.13189/ujar.2022.100310
Viedienieiev, V.A., Piskunova, O.V.: Forecasting the selling price of the agricultural products in ukraine using deep learning algorithms. Univer. J. Agricult. Res. 9(3), 91–100 (2021). https://doi.org/10.13189/ujar.2021.090304
Shukla, S.K., Lalan, S., Awasthi, S.K., Pathak, A.D.: Sugarcane in India (Package of Practices for Different Agro-climatic Zones), AICRP (S) Technical Bulletin - No. 1, Published by ICAR-All India Coordinated Research Project on Sugarcane (ICAR-Indian Institute of Sugarcane Research), pp. 1–17 (2017)
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Sharma, D.K., Punhani, A. (2024). Deep Learning Methods for Precise Sugarcane Disease Detection and Sustainable Crop Management. In: Garg, D., Rodrigues, J.J.P.C., Gupta, S.K., Cheng, X., Sarao, P., Patel, G.S. (eds) Advanced Computing. IACC 2023. Communications in Computer and Information Science, vol 2054. Springer, Cham. https://doi.org/10.1007/978-3-031-56703-2_4
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