Digital image processing techniques for detecting, quantifying and classifying plant diseases

By J. G. Arnal Barbedo. SpringerPlus, 2013

603 citations recorded by the source corpus.

This paper presents a survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey. The selected proposals are divided into three classes according to their objective: detection, severity quantification, and classification. Each of those classes, in turn, are subdivided according to the main technical solution used in the algorithm. This paper is expected to be useful to researchers working both on vegetable pathology and pattern recognition, providing a comprehensive and accessible overview of this important field of research.

Subfields: Analytical Chemistry, Plant Science

View Paper: https://www.semanticscholar.org/paper/8f8a6255c70502d96c5ecdb6dff25fe47eb45fdb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

In Plant Science

Citations in the corpus

A review on the main challenges in automatic plant disease identification based on visible range images (2016, methodology): “Because of space limitations, technical details about the methods, including the software used, were omitted, but a comprehensive discussion on the matter can be found in Barbedo (2013).”
Factors influencing the use of deep learning for plant disease recognition (2018, background): “It is also worth noting that many agricultural areas are too expansive to be properly monitored throughout (Barbedo, 2013).”
Plant Disease Detection Using Machine Learning (2018, background): “Texture [7] is calculated based on the frequency of the pixel I occupying the position next to pixel J.”
Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning. (2017, methodology): “Machine learning techniques have been used to detect, classify, and quantify a wide variety of diseases on many crops, as reviewed by Barbedo (2013) and Singh et al. (2016).”
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, methodology): “Computer Vision, along with Artiﬁcial Intelligence (AI), has been developing techniques and methods for recognizing and classifying objects with signiﬁcant advances [5].”
Deep convolution neural network for image recognition (2018, background): “4) because they are the widely adapted for image classification with good performances by making convolutional networks fast to train (Barbedo, 2013; Krizhevsky et al., 2012).”
Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification (2016, methodology): “Exploiting common digital image processing techniques such as colour analysis and thresholding [9] were used with the aim of detection and classi cation of plant diseases.”
Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review (2018, neutral): “For the classi ﬁ cation, three models were analyzed: SVM, KNN (k-Nearest Neural Network) and PNN (Probabilistic Neural Network).”
Disease detection on the leaves of the tomato plants by using deep learning (2017, methodology): “For example, image processing used for the weed detection and the fruit grading [5], for the detecting, quantifying and classifying plant diseases [6], and for the phenotyping of plant disease symptoms [7].”
Plants Disease Identification and Classification Through Leaf Images: A Survey (2018, neutral): “The detection of plants disease (i.e., unusual growth or dysfunction) thus compelled many researchers to employ image processing techniques to ease this difﬁcult task [1–10].”
Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques (2019, neutral): “2017; Barbedo 2013).”