An image-processing based algorithm to automatically identify plant disease visual symptoms.

By Anyela Camargo, Jeremy S. Smith. 2009

465 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Cell Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/97bccf906b2514ed6d38e0cc18d4a4d021210ec4

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

1. Dheeb Al Bashish
2. Changshui Zhang
3. Malik Braik
4. J. G. Arnal Barbedo
5. Shreelekha Pandey
6. Sukhvir Kaur
7. Shivani Goel
8. Sulieman Bani-Ahmad

In Cell Biology

1. Dheeb Al Bashish
2. Malik Braik
3. J. G. Arnal Barbedo
4. Shreelekha Pandey
5. Sukhvir Kaur
6. Shivani Goel
7. Sulieman Bani-Ahmad

In Plant Science

1. Shreelekha Pandey
2. Sukhvir Kaur
3. Shivani Goel
4. Dheeb Al Bashish
5. Malik Braik
6. J. G. Arnal Barbedo
7. Sulieman Bani-Ahmad
8. Wubetu Barud Demilie

Citations in the corpus

• A review on the main challenges in automatic plant disease identification based on visible range images (2016, methodology): “This may affect the accuracy of thresholding and other procedures, even if some kind of adaptive scheme is adopted (Barbedo, 2014; Camargo & Smith, 2009).”
• Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach (2021, background): “Preprocessing, such as image enhancement, color transformation, and segmentation [5], is a prerequisite before efficiently extracting features.”
• Cotton leaf disease identification using pattern recognition techniques (2015, unknown): “Therefore the need for fast, automatic, less expensive and accurate method to detect plant disease cases is of great realistic significance [4] [5].”
• A framework for detection and classification of plant leaf and stem diseases (2010, methodology): “The proposed framework is image-processingbased and is composed of the following main steps; in the first step the images at hand are segmented using the K-Means technique, in the second step the segmented images are passed through a pre-trained neural network.”
• Plants Disease Identification and Classification Through Leaf Images: A Survey (2018, methodology): “Automatic segmentation comparable to manual is achieved by converting RGB to a new color space H, I3a and I3b [28].”
• An In-field Automatic Wheat Disease Diagnosis System (2017, background): “With the development of computer vision, concerns are growing about the image-based detection technologies for crop disease (Camargo and Smith, 2009; Arivazhagan et al., 2013; Barbedo, 2014; Rastogi et al., 2015), which gets rid of the shackles of time cost and molecular analysis equipments…”
• Digital image processing techniques for detecting, quantifying and classifying plant diseases (2013, methodology): “Camargo and Smith (2009a) proposed a method to identify regions of leaves containing lesions caused by diseases.”