A review on the main challenges in automatic plant disease identification based on visible range images

By J. G. Arnal Barbedo. 2016

609 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Industrial and Manufacturing Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/97543c7a7d01b00c9b5668f86661457f9244f956

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In Biomedical Engineering

In Industrial and Manufacturing Engineering

In Plant Science

Citations in the corpus

Detection of sprout damage in wheat kernels using NIR hyperspectral imaging (2018, background): “This visual selection, being a subjective task, is prone to psychological and cognitive phenomena that may lead to bias and optical illusions (Barbedo, 2016).”
Factors influencing the use of deep learning for plant disease recognition (2018, background): “In previous works, we have investigated the characteristics of the proposals found in the literature (Barbedo, 2013) and the main challenges that still prevent this kind of technology from being adopted in practice (Barbedo, 2016).”
Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review (2018, positive): “Barbedo (2016) addressed the main challenges in automatic disease identi ﬁ cation.”
An In-field Automatic Wheat Disease Diagnosis System (2017, background): “To make an automatic crop disease diagnosis system that can be applied to in-ﬁeld images, one has to face some intractable challenges summarized in (Barbedo, 2016): (1) complex image backgrounds, e.g. leaves, soils, stones and even the people’s hands, (2) uncontrollable capture conditions, e.g.…”
Plants Disease Identification and Classification Through Leaf Images: A Survey (2018, background): “This fact further complicates the applicability of computer vision techniques in their proper recognition [11, 12].”
A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, background): “Thus, there is an appearance of similar symptoms for two or more diseases [15].”
Plant disease detection and classification techniques: a comparative study of the performances (2024, background): “Some factors and issues may have an impact on disease identification and classification; the majority of them have been summarised in the studies of [19, 27, 28, 31–38].”
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, background): “The problem with the visual assessment to identify diseases is that the evaluating subject has assumed a subjective task, prone to psychological and cognitive phenomena that can lead to prejudice, optical illusions, and, finally, error[18].”
Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease (2019, background): “Therefore, Barbedo in [15] have present various issues and challenges in the classification of plant diseases.”
Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress (2019, background): “In Barbedo (2016) the six main challenges encountered in this type of problem are addressed whereas these challenges were separated into extrinsic and intrinsic factors.”
Convolutional Neural Networks for the Automatic Identification of Plant Diseases (2019, methodology): “For more information on these techniques (see Sankaran et al., 2010; Mahlein et al., 2012; Martinelli et al., 2015; Barbedo, 2016).”