Cucumber Leaves Diseases Detection through Computational Approaches: A Review

Main Article Content

R. Manavalan

Abstract

Agriculture is the largest economic sector since it generates both food and raw materials for industry. Plants are a primary source of food, energy, vitamins, and minerals. Cucumber is a widely produced food crop worldwide. Cucumber plants grow naturally in both temperate and tropical environments. Many diseases and pests can harm cucumbers at any stage of development, reducing their growth and causing major economic losses in agriculture. Anthracnose, target leaf spot, sticky stem blight and angular leaf spot are bacterial diseases that can cause serious damage to plant leaves. Thus, early detection and treatment of cucumber diseases are critical for yield enhancement. The diagnosis of cucumber leaf diseases by naked eyes results in improper pesticide measurement. To maximize productivity, early detection of cucumber illnesses is required. This leads to inaccuracy in pesticide dosage control. It is necessary to automate disease detection and diagnosis in order to raise cucumber production and quality. These algorithms extract features from cucumber plant photos and identify diseases early on. An overview of image processing and machine learning techniques used to detect disease in cucumber leaves. This paper also discusses the future potential for computer algorithms for evaluating cucumber leaf images.

Keywords:
Cucumber, plant diseases, computational methods, SVM, neural network

Article Details

How to Cite
Manavalan, R. (2021). Cucumber Leaves Diseases Detection through Computational Approaches: A Review. Asian Journal of Research in Biosciences, 3(2), 100-110. Retrieved from https://globalpresshub.com/index.php/AJORIB/article/view/1302
Section
Mini Review Papers

References

Sannakki S, Rajpurohit V, Nargund V, Kumar A, Yallur P. Leaf Disease Grading by Machine Vision and Fuzzy Logic. Int J. 2010 Nov 30;2.

Wikipedia. Cucumber [Internet]. 2020. Available: en.wikipedia.org/wiki/Cucumber

Medicalnewstoday.com. Cucumbers: Health Benefits, Nutritional Content, and Uses. 2019.

Facts O. Cucumbers: Nutrition, Health Benefits, & Recipes [Internet]; 2011. Available:www.organicfacts.net/health-benefits/vegetable/cucumber.html

GardenTech.com. Identify and Control Alternaria Leaf Spot [Internet]. Available:www.gardentech.com/disease/alternaria-leaf-spot

Apsnet.org. Downy Mildew of Cucurbits. [Internet]. Available:www.apsnet.org/edcenter/disandpath/oomycete/pdlessons/Pages/Cucurbits.aspx

Pacific Northwest Pest Management H. Cucumber (Cucumis Sativus)-Fusarium Wilt [Internet]. Available:pnwhandbooks.org/plantdisease/host-disease/cucumber-cucumis-sativus-fusarium-wilt

Plantvillage.psu.edu. Cucumber | Diseases and Pests, Description, Uses, Propagation [Internet]. Available:Plantvillage.psu.edu, plantvillage.psu.edu/topics/cucumber/infos.

Ghiggins. Cucurbits, Leaf Spots [Internet]. Center for Agriculture, Food, and the Environment. 2016. Available:ag.umass.edu/vegetable/fact-sheets/cucurbits-leaf-spots. Accessed 28 Sept. 2021.

McRae S. How to Identify and Treat Common Cucumber Diseases [Internet]. Dengarden. Available:dengarden.com/gardening/Plant-Diseases-that-Affect-Cucumbers-and-How-to-Treat-Them

Asraf HM, Nooritawati MT, Rizam MSBS. A comparative study in kernel-based Support Vector Machine of oil palm leaves nutrient disease. Procedia Eng. 2012;41(Iris):1353–9.

Youwen T, Tianlai L, Yan N. The recognition of cucumber disease based on image processing and Support Vector Machine. Proc - 1st Int Congr Image Signal Process CISP 2008. 2008;2:262– 7.

Zhang J, Zhang W. Support vector machine for recognition of cucumber leaf diseases. Proc - 2nd IEEE Int Conf Adv Comput Control ICACC. 2010;2010;5(1):264–6.

HaiYue J, Kai S. IBLE algorithm in agricultural disease diagnosis. Proc - 3rd Int Conf Intell Networks Intell Syst ICINIS. 2010;2010;401–4.

Pixia D, Xiangdong W. Recognition of Greenhouse Cucumber Disease Based on Image Processing Technology. Open J Appl Sci. 2013;03(01):27–31.

Wu N, Li M, Chen L, Yuan Y, Song S. A LDA-based segmentation model for classifying pixels in crop diseased images. Chinese Control Conf CCC. 2017;11499–505.

Palanisamy P, Thangavel K, Perumal P, Manavalan R. A novel approach to select significant genes of leukemia cancer data using K-Means clustering. Proc 2013 Int Conf Pattern Recognition, Informatics Mob Eng PRIME 2013. 2013;104–8.

Sekulska-Nalewajko J, Goclawski J. A semi-automatic method for the discrimination of diseased regions in detached leaf images using fuzzy c-means clustering. 2011 Proc 7th Int Conf Perspect Technol Methods MEMS Des MEMSTECH 2011. 2011 Jan 1;

Abdulla S, K. S, M. N. Automatic Cucurbit Leaf Disease Detection using Image; 2015.

Bai X, Li X, Fu Z, Lv X, Zhang L. A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images. Comput Electron Agric [Internet]. 2017;136:157–65. Available:http://dx.doi.org/10.1016/j.compag.2017.03.004

Zhang S, Wu X, You Z, Zhang L. Leaf image based cucumber disease recognition using sparse representation classification. Comput Electron Agric. 2017 Mar 1;134:135–41.

Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H. State-of-the-art in artificial neural network applications: A survey. Heliyon [Internet]. 2018;4(11):e00938. Available:https://www.sciencedirect.com/science/article/pii/S2405844018332067

Wei Y, Chang R, Wang Y, Liu H, Du Y, Xu J, et al. A study of image processing on identifying cucumber disease. IFIP Adv Inf Commun Technol. 2012;370 AICT(PART 3):201–9.

Asefpour Vakilian K, Massah J. An artificial neural network approach to identify fungal diseases of cucumber (Cucumis sativus L.) plants using digital image processing. Arch Phytopathol Plant Prot. 2013;46(13):1580–8.

Shi Y, Wang XF, Zhang SW, Zhang CL. PNN based crop disease recognition with leaf image features and meteorological data. Int J Agric Biol Eng. 2015;8(4):60–8.

Ma J, Du K, Zheng F, Zhang L, Gong Z, Sun Z. A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric [Internet]. 2018;154(August):18–24. Available: https://doi.org/10.1016/j.compag.2018.08.048

Lin K, Gong L, Huang Y, Liu C, Pan J. Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural network. Front Plant Sci. 2019;10(February):1–10.

Zeng W, Li M. Crop leaf disease recognition based on Self-Attention convolutional neural network. Comput Electron Agric [Internet]. 2020;172:105341. Available:https://doi.org/10.1016/j.compag.2020.105341

Azhagi TT, Swetha K, Shravani M, Madhavi & AT. Plant Pathology Detection and Control Using Raspberry Pi. Int J Eng Sci Res Technol [Internet]. 2018;7(3):519–25. Available:http://www.ijesrt.xn--com-1ea

Manavalan R. Efficient detection of sugarcane diseases through intelligent approaches : A Review. 2021;3(4):27–37.

Velusamy K, Manavalan R. Performance Analysis of Unsupervised Classification Based on Optimization. Int J Comput Appl. 2012;42:22–7.