Abstract
The automatic identification of crop leaf disease detection (CLDD) is very useful for the agricultural field. At present deep learning is a very hot research topic to discuss, it can successfully solve the problems of farmers. This paper presents the Deep Convolutional Neural Network (DCNN) for improving the accuracy of crop leaf disease detection. Further, the consequence of the offered algorithm is analyzed using data augmentation to minimize the data scarcity problem that arises due to uneven dataset size. The consequence of the offered strategy is analyzed on the PlantVillage dataset for the tomato plant based on accuracy, recall, precision, and F1-score. It is observed that the offered strategy provides noteworthy improvement over the traditional state of arts.
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References
Applalanaidu MV, Kumaravelan G (2021) A review of machine learning approaches in plant leaf disease detection and classification. In: 2021 third international conference on intelligent communication technologies and virtual mobile networks (ICICV). IEEE, pp 716–724. https://doi.org/10.1109/ICICV50876.2021.9388488
Yaqoob N, Ali SA, Kannaiah D, Khan N, Shabbir MS, Bilal K, Tabash MI (2022) The effects of agriculture productivity, land intensification, on sustainable economic growth: a panel analysis from Bangladesh, India, and Pakistan economies. Environ Sci Pollut Res: 1–9. https://doi.org/10.1007/s11356-021-18471-6
Mapari R, Bhangale K, Deshmukh L, Gode P, Gaikwad A (2022) Agriculture protection from animals using smart scarecrow system. In: Soft computing for security applications. Springer, Singapore, pp 539–551
Jogekar RN, Tiwari N (2021) A review of deep learning techniques for identification and diagnosis of plant leaf disease. Smart Trends Comput Commun Proc SmartCom 2020:435–441
Li L, Zhang S, Wang B (2021) Plant disease detection and classification by deep learning—a review. IEEE Access 9:56683–56698. https://doi.org/10.1109/ACCESS.2021.3069646
Deepalakshmi P, Lavanya K, Srinivasu PN (2021) Plant leaf disease detection using CNN algorithm. Int J Inf Syst Model Des (IJISMD) 12(1):1–21. https://doi.org/10.4018/IJISMD.2021010101
Lu J, Tan L, Jiang H (2021) Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture 11(8):707. https://doi.org/10.3390/agriculture11080707
Yogeshwari M, Thailambal G (2021) Automatic feature extraction and detection of plant leaf disease using GLCM features and convolutional neural networks. Mater Today Proc
Kartikeyan P, Shrivastava G (2021) Review on emerging trends in detection of plant diseases using image processing with machine learning. Int J Comput Appl 975:8887. https://doi.org/10.5120/ijca2021920990
Ouhami M, Hafiane A, Es-Saady Y, Hajji ME, Canals R (2021) Computer vision, IoT and data fusion for crop disease detection using machine learning: a survey and ongoing research. Remote Sens 13(13):2486. https://doi.org/10.3390/rs13132486
Kulkarni AH, Patil A (2012) Applying image processing technique to detect plant diseases. Int J Mod Eng Res 2(5):3661–3664
Arivazhagan S, Newlin Shebiah R, Ananthi S, Vishnu Varthini S (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR J 15(1):211–217
Tripathi G, Save J (2015) An image processing and neural network based approach for detection and classification of plant leaf diseases. J Impact Factor 6(4):14–20
Pooja V, Das R, Kanchana V (2017) Identification of plant leaf diseases using image processing techniques. In: 2017 IEEE technological innovations in ICT for agriculture and rural development (TIAR). IEEE, pp 130–133
Singh MK, Chetia S (2017) Detection and classification of plant leaf diseases in image processing using MATLAB. Int J Life Sci Res 5(4):120–124. https://doi.org/10.21786/bbrc/13.14/28
Dhaware CG, Wanjale KH (2017) A modern approach for plant leaf disease classification which depends on leaf image processing. In: 2017 international conference on computer communication and informatics (ICCCI). IEEE, pp 1–4. https://doi.org/10.1109/ICCCI.2017.8117733
Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Van Esesn BC, Awwal AAS, Asari VK (2018) The history began from alexnet: a comprehensive survey on deep learning approaches. ar**v preprint ar**v:1803.01164
Hang J, Zhang D, Chen P, Zhang J, Wang B (2019) Classification of plant leaf diseases based on improved convolutional neural network. Sensors 19(19):4161. https://doi.org/10.3390/s19194161
Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419. https://doi.org/10.3389/fpls.2016.01419
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I am very grateful to Siddhant College of Engineering, Sudumbare, Pune for continuous support of research work.
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Bade, A.V., Suresh Kumar, M. (2024). Crop Leaf Disease Detection Using DCCN. In: Dhote, N.K., Kolhe, M.L., Rehman, M. (eds) Recent Evolutions in Energy, Drives and e-Vehicles. REEDEV 2022. Lecture Notes in Electrical Engineering, vol 1162. Springer, Singapore. https://doi.org/10.1007/978-981-97-0763-8_5
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DOI: https://doi.org/10.1007/978-981-97-0763-8_5
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