Abstract
In our nation, the agricultural sector is decreasing which reciprocally affects the production capability of agricultural goods. It has become significantly more important to feed the steadily growing population. Plant diseases affect the livelihood of this imperative source more. It results in depletion of production and economic losses in agriculture. We have to protect and control the crops losses in order to aggregate the agricultural productions. Using agronomists, the traditional method, takes a lot of time to continuously observe the crops and moreover finding an agronomist in rural areas is also very challenging. Therefore, developing a system which can use visible or noticeable symptoms to identify and distinguish plant disease habitually to offer appropriate support will unquestionably help all the farmers to reduce crop losses and will increase production. Currently, the deep learning application in crop disease classification is the most active areas of research for which the prerequisite is an image dataset. A custom CNN architecture is proposed in this article to classify ladies finger plant leaf image into three categories namely healthy, disease and leaf burn. The dataset contains 1088 samples of ladies finger plant leaf images, among which 457 images are identified as healthy (non-disease) leaves, 509 images as disease and pest infected and 122 images as leaf burn due to fertilizer overdose. The images were taken directly from the agriculture farms of different villages of Tiruvannamalai district, Tamil Nadu, India. The proposed CNN architecture achieved 96% classification accuracy.
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References
Adilakshmi A, Korat DM, Vaishnav PR (2008) Effect of organic manures and inorganic fertilizers on insect pests infesting okra. Karnataka J Agric Sci 21:287–289
Andrews G (1998) Understanding nitrogen fertilizers, gardening and water quality protection. Oregon state university, USA
Anonymous, area and production of horticulture crops (2018) (3rd Advance Estimates) National Horticulture Board. Indian Horticulture Database. In: Ministry of Agriculture and Farmers Welfare, Gurgaon, India
Aravind K, Aniirudh RP, Mukesh R, Ashiwin K, Vikas RG (2019) Grape crop disease classification using transfer learning approach. In: Pandian D, Fernando X, Baig Z, Shi F (eds) Proceedings of the international conference on ISMAC in computational vision and bio-engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture notes in computational vision and biomechanics, vol 30. Springer, Cham
Atabay HA (2016) Binary shape classification using convolutional neural networks. IIOAB J 7(5):332–336
Charrier A (1984) Genetic resources of Abelmoschus (okra). IBPGR Secretarial, Paris
Chen J, Liu Q, Gao L (2019) Visual tea leaf disease recognition using a convolutional neural network model. Symmetry. https://doi.org/10.3390/sym11030343
Department of Biotechnology (2009) Series of crop specific biology documents biology of okra. Ministry of Science and Technology Government of India
Dhandapani N, Shelkar UR, Muruga M (2003) Bio-intensire pest management (BIPM) in major vegetable crop: an Indian perspective. Food Agric Environ 2:333–339
Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318. https://doi.org/10.1016/j.compag.2018.01.009
Ghanem GAM (2003) Okra leaf curl virus: a monopartite begomovirus infecting okra crop in Saudi Arabia. Arab J Biotechnol 6:139–152
Guo Y et al (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27–48. https://doi.org/10.1016/j.neucom.2015.09.116
Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60:84–90
Kumar S, Dagnoko S, Haougui A, Ratnadass A, Pasternak D, Kouame C (2010) Okra (Abelmoschus spp.) in West and Central Africa: potential and progress on its improvement. Afr J Agric Res 5:3590–3598
Lamont W (1999) Okra a versatile vegetable crop. Hort Technol 9:179–184
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Liang W, Zhang H, Zhang GF, Cao HX (2019) Rice blast disease recognition using a deep convolutional neural network. Sci Rep 9:2869. https://doi.org/10.1038/s41598-019-38966-0
Liu B, Zhang Y, He D, Li Y (2017) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry. https://doi.org/10.3390/sym10010011
Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–384. https://doi.org/10.1016/j.neucom.2017.06.023
Ma J et al (2018) A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric 154:18–24. https://doi.org/10.1016/j.compag.2018.08.048
Mohanty SP, Hughes DP, Salathe M (2016a) Using deep learning for image-based plant disease detection. Front Plant Sci 1:1419. https://doi.org/10.3389/fpls.2016.01419
Mohanty SP, Hughes DP, Salathe M (2016b) Using deep learning for image-based plant disease detection. Front Plant Sci 1:1419. https://doi.org/10.3389/fpls.2016.01419
N’Guessan KP, Fargette D, Fauquet C, Thouvenel JC (1992) Aspects of the epidemiology of okra leaf curl virus in Côted’Ivoire. Trop Pest Manag 38:122–126
Picon A et al (2019) Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput Electron Agric 161:280–290. https://doi.org/10.1016/j.compag.2018.04.002
Powers DMW (2011) Evaluation: from precision, recall and f-factor to roc, informedness, markedness & correlation. J Mach Learn Technol 2(1):37–63
Prakash S, Sangeetha K (2020) Deep multilayer and nonlinear Kernelized Lasso feature learning for healthcare in big data environment. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02328-w
Rashid MH, Yasmin L, Kibria MG, Mollik AKMSR, Hossain SMM (2002) Screening of okra germplasm for resistance to yellow vein mosaic virus under field conditions. Pak J Plant Pathol 1:61–62
Savary S, Ficke A, Aubertot JN et al (2012) Crop losses due to disease and their implications for global food production losses and food security. Food Secur 4(4):519–537. https://doi.org/10.1007/s12571-012-0200-5
Shah AM, Yan X, Shah SAA, Mamirkulova G (2020) Mining patient opinion to evaluate the service quality in healthcare: a deep-learning approach. J Ambient Intell Human Comput 11:2925–2942. https://doi.org/10.1007/s12652-019-01434-8
Strange RN, Scott PR (2005) Plant disease: a threat to global food security. Annu Rev Phytopathol 43:83–116
Too EC, Yujian L, Njuki. & Yingchun LA (June 2019) Comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric 161:272–279. https://doi.org/10.1016/j.compag.2018.03.032
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Selvam, L., Kavitha, P. Classification of ladies finger plant leaf using deep learning. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02671-y
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DOI: https://doi.org/10.1007/s12652-020-02671-y