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Owomugisha, G. and Mwebaze, E. (2016) Machine Learning for Plant Disease Incidence and Severity Measurements from Leaf Images. 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, 18-20 December 2016, 158-163.

has been cited by the following article:

• TITLE: Plant Disease Severity Assessment Based on Machine Learning and Deep Learning: A Survey

  AUTHORS: Demba Faye, Idy Diop, Nalla Mbaye, Doudou Dione, Marius Mintu Diedhiou

  KEYWORDS: Plant, Disease, Severity, Machine Learning, Deep Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.9, September 26, 2023

  ABSTRACT: The world’s agricultural production suffers huge losses estimated between 20% and 40% annually. 40% to 50% of such losses are due to pest and diseases which cause significant economic losses every year. Precise assessment of severity is crucial for suitable management of crop diseases. It helps famers to avoid yield losses, reduce production costs, ensure good disease management and so on. This paper is a review of plant diseases severity estimation solutions proposed by researchers the last few years and based on Image Processing Techniques (IPT), classical Machine Learning (ML) and Deep Learning (DL) algorithms. The analysis of these solutions has allowed us to identify their limitations and potential challenges in plant disease severity assessment.