Identification of Vegetables Diseases through Computational Approaches: A Review
R. Manavalan *
Department of Computer Science, Arignar Anna Government Arts College, Villupuram – 605 602, Tamilnadu, India.
*Author to whom correspondence should be addressed.
Abstract
The agriculture sector is closely connected with all parts of society and influences more in the country's economic growth. The vegetables and fruits are usually fortified by high sources of vitamins and minerals to stay remain healthy. Vegetable production is significantly hampered by different diseases and pests. So, farmers suffer serious economic losses. Early detection of a range of infections in vegetable plants and the appropriate pest management strategies are essential to increase productivity. Diseases on vegetables cannot be detected with the naked eye, resulting in erroneous pesticide control measurements. Thus, it is crucial to accurately identify and diagnose diseases in plants to boost productivity and quality. The numerous Image processing techniques efficiently extract features from the leaves of the vegetable and recognize the diseases at an early stage. Multiple image processing algorithms extract useful information from the leaves of vegetable plants, and this information is then utilized to identify various diseases. This article provides an overview of several specialized computational methods for diagnosing various illnesses in vegetable plant leaves. The issues that arise when using computational tools to analyze vegetable leaf diseases are also discussed, along with future directions.
Keywords: Relatives of Isoetes pantii, Plant diseases, spiny spores in lycophyta New species of lsoetes, computational approach, plant leaves, image processing, feature extraction, classification
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Pretty J. Agricultural sustainability: concepts, principles and evidence. Philos Trans R Soc Lond B Biol Sci [Internet]. 2008;363(1491):447–65.
Available:https://pubmed.ncbi.nlm.nih.gov/17652074
Hase A, Aher P, Hase S. Detection, categorization and suggestion to cure infected plants of tomato and grapes by using OpenCV framework for andriod environment. 2017;956–959.
Schreinemachers P, Simmons EB, Wopereis MCS. Tapping the economic and nutritional power of vegetables. Glob Food Sec [Internet]. 2018;16:36– 45.
Available:https://www.sciencedirect.com/science/article/pii/S2211912417300640
Müller O, Krawinkel M. Malnutrition and health in developing countries. CMAJ [Internet]. 2005;173(3):279–86.
Available:https://pubmed.ncbi.nlm.nih.gov/16076825
Manavalan R. Automatic identification of diseases in grains crops through computational approaches: A review. Comput Electron Agric [Internet]. 2020; 178(September):105802.
Available:https://doi.org/10.1016/j.compag.2020.105802
Sudhesh R, Nagalakshmi V, Amirthasaravanan A. A systematic study on disease recognition, categorization, and quantification in agricultural plants using image processing. 2019 IEEE Int Conf Syst Comput Autom Networking, ICSCAN 2019. 2019;1–5.
Natural P. Anthracnose Disease: Symptoms, Treatment and Control.
Natural P. Vegetable Garden Plant Disease Problems [Internet]. Planet Natural.
Available:www.planetnatural.com/vegetable-gardening-guru/plant-diseases/
Www.rhs.org.uk. Downy Mildews [Internet]. Available:www.rhs.org.uk/advice/profile?pid=683
JS F aka. 17 Common Diseases of Leafy Vegetables: Photos, Prevention, and Treatment.
Guru VG. Common Plant Diseases. Vegetable Garden Guru.
Planet Natural. Fusarium Wilt: Symptoms, Treatment and Control. planetnatural.com.
Almanac OF. Powdery Mildew. Old Farmer’s Almanac; 2018.
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.
Veni S, Vishnu Priya PM, Aishwarya Mala GM, Kayartaya A, Anusha R. Computer aided system for detection and classification of brinjal leaf diseases using thermal and visible light images. J Theor Appl Inf Technol. 2017;95(19):5224–36.
Islam M, Dinh A, Wahid K, Bhowmik P. Detection of potato diseases using image segmentation and multiclass support vector machine. Can Conf Electr Comput Eng. 2017;8–11.
Abdu AM, Mokji MM, Sheikh UU. Automatic vegetable disease identification approach using individual lesion features. Comput Electron Agric [Internet]. 2020; 176(July):105660.
Available:https://doi.org/10.1016/j.compag.2020.105660
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.
Khatra A. A Novel Machine Vision System for Radish Crop Quality Monitoring based on Leaf Inspection. IOSR J Eng. 2012; 02(02):372–5.
Samanta D, Chaudhury PP, Ghosh A. Scab diseases detection of potato using image processing. Int J Comput Trends Technol. 2012;3(1):109–13.
Husin Z, Shakaff AYM, Aziz AHA, Farook RSM. Plant Chili Disease Detection using the RGB Color Model. Int J Digit Content Technol its Appl. 2013;7(10):107–17.
Husin Z Bin, Md Shakaff AY Bin, Abdul Aziz AH Bin, Mohamed Farook RBS. Feasibility study on plant chili disease detection using image processing techniques. Proc - 3rd Int Conf Intell Syst Model Simulation, ISMS 2012. 2012; 291–6.
Length F. Color image segmentation using perceptual spaces through applets for determining and preventing diseases in chili peppers. African J Biotechnol. 2013; 12(7):679–88.
Pujari JD, Yakkundimath R, Byadgi AS. Neuro-kNN classification system for detecting fungal disease on vegetable crops using local binary patterns. Agric Eng Int CIGR J. 2014;16(4):299–308.
Joshi S, Jamadar G, Nachan S. Chilli disease detection 1. 2015;(2):34–7.
Singh J, Kaur H. Plant Disease Detection Based on Region-Based Segmentation and KNN Classifier. In: Lecture Notes in Computational Vision and Biomechanics. 2019;1667–75.
Suttapakti U, Bunpeng A. Potato Leaf Disease Classification Based on Distinct Color and Texture Feature Extraction. Proc - 2019 19th Int Symp Commun Inf Technol Isc 2019. 2019;(Mcd):82–5.
Manavalan R, Thangavel K. Evaluation of Textural Feature Extraction Methods for Prostate Cancer TRUS Medical images. Int J Comput Appl. 2011;36:33–9.
Muthukannan K, Latha P. A PSO model for disease pattern detection on leaf surfaces. Image Anal Stereol. 2015;34(3): 209–16.
Xie C, Shao Y, Li X, He L. Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging. Sci Rep. 2015;5:16564.
Yadav R, Rana Y, Nagpal S. Plant Leaf Disease Detection and Classification Using Particle Swarm Optimization. In 2019; 294–306.
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
Ataş M, Yardimci Y, Temizel A. Aflatoxin contaminated chili pepper detection by hyperspectral imaging and machine learning. 2011;80270F-80270F.
Athanikar G, Badar MP. Potato Leaf Diseases Detection and Classification System. In 2016.
Anand R, Veni S, Aravinth J. An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method. 2016 Int Conf Recent Trends Inf Technol ICRTIT 2016.
El I, Es-saady Y, El M, Mammass D, Benazoun A. Automatic Recognition of Vegetable Crops Diseases based on Neural Network Classifier. Int J Comput Appl. 2017;158(4):48–51.
Muthukannan K, Latha P. A GA_FFNN algorithm applied for classification in diseased plant leaf system. Multimed Tools Appl. 2018;77(18):24387–403.
Ashqar BAM, Abu-Naser SS. Image-Based Tomato Leaves Diseases Detection Using Deep Learning. Int J Acad Eng Res [Internet]. 2018;2(12):10–6.
Available:www.ijeais.org/ijaer
Al-Amin M, Bushra TA, Hoq MN. Prediction of Potato Disease from Leaves using Deep Convolution Neural Network towards a Digital Agricultural System. 1st Int Conf Adv Sci Eng Robot Technol 2019, ICASERT 2019. 2019;2019(Icasert): 1–5.
Mukti IZ, Biswas D. Transfer Learning Based Plant Diseases Detection Using ResNet50. 2019 4th Int Conf Electr Inf Commun Technol EICT 2019. 2019; (December):1–6.
Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S. ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network. Procedia Comput Sci [Internet]. 2020;167(2019):293–301.
Available:https://doi.org/10.1016/j.procs.2020.03.225
Sharma P, Hans P, Gupta SC. Classification of plant leaf diseases using machine learning and image preprocessing techniques. Proc Conflu 2020 - 10th Int Conf Cloud Comput Data Sci Eng. 2020; 480–4.
Ranjan Dasgupta S, Rakshit S, Mondal D, Kole D. Detection of Diseases in Potato Leaves Using Transfer Learning. In 2020. p. 675–84.
Agarwal M, Sinha A, Gupta S, Mishra D, Mishra R. Potato Crop Disease Classification Using Convolutional Neural Network. 2020;391–400.
Foysal MF, Islam M, Abujar S, Hossain S. A Novel Approach for Tomato Diseases Classification Based on Deep Convolutional Neural Networks. 2019; 583–591.
Jaiswal A, Pathak S, Rathore Y, Janghel R. Detection of Disease from Leaf of Vegetables and Fruits Using Deep Learning Technique. 2021;199–206.
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.