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A Survey on Detecting Plant Diseases Detection and Proposal of a Solution using Recommendation System

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Hanif Khan Pathan, Dhanraj Verma

Hanif Khan Pathan and Dhanraj Verma. A Survey on Detecting Plant Diseases Detection and Proposal of a Solution using Recommendation System. International Journal of Computer Applications 183(38):39-44, November 2021. BibTeX

	author = {Hanif Khan Pathan and Dhanraj Verma},
	title = {A Survey on Detecting Plant Diseases Detection and Proposal of a Solution using Recommendation System},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2021},
	volume = {183},
	number = {38},
	month = {Nov},
	year = {2021},
	issn = {0975-8887},
	pages = {39-44},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2021921784},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Agriculture is the backbone of the Indian economy. A significant amount of people in India depends on agricultural income. But due to traditional methods of farming and dependency on nature, infection, and other different diseases, the production, and profit of crops are affected, results in poor quality and productivity. This paper is motivated to investigate the recent advancement in the agriculture based on computational technology. Therefore, recent technique of efficient and accurate plant disease detection using Machine Learning (ML) and image processing techniques has proposed to study. A survey has been carried out and the summary of conducted review has been reported. The reviewed literature is focused on categorizing the methods based on ML algorithms used. Additionally, the trends of utilization of ML techniques are also described. Using the concluded reviews and available facts we proposed a ML model for early disease prediction, and also proposed a recommendation system which provides relevant solution to deal with the disease is described. The different components of both the models and required functional aspects are also discussed. Finally, the paper provides the conclusion of the work carried out and future guidelines.


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Review, survey, plant disease detection and recommendation, machine learning algorithms, feature selection.