Digital image processing techniques for detecting, quantifying and classifying plant diseases.
TLDR
A survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum, providing a comprehensive and accessible overview of this important field of research.Abstract:
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.read more
Citations
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Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
TL;DR: A new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks, which is able to recognize 13 different types of plant diseases out of healthy leaves.
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Plant Disease Detection by Imaging Sensors - Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping.
TL;DR: The most relevant areas of application of sensor-based analyses are precision agriculture and plant phenotyping as discussed by the authors, which is facilitated by highly sophisticated and innovative methods of data analysis that lead to new insights derived from sensor data for complex plant-pathogen systems.
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Identification of rice diseases using deep convolutional neural networks
TL;DR: A novel rice diseases identification method based on deep convolutional neural networks (CNNs) techniques, trained to identify 10 common rice diseases with much higher accuracy than conventional machine learning model.
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Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review
TL;DR: This work presents a systematic review that aims to identify the applicability of computer vision in precision agriculture for the production of the five most produced grains in the world: maize, rice, wheat, soybean, and barley.
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A review on the main challenges in automatic plant disease identification based on visible range images
TL;DR: An analysis of the challenges faced by automatic plant disease identification using visible range images, emphasizing both the problems that they may cause and how they may have potentially affected the techniques proposed in the past.
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