The goal has been to detect disease in mango trees. This paper compares different approaches to extract color features and check the accuracy and applicability for mango trees. The paper proposes variations which helped in increasing the accuracy of features extracted for mango trees: firstly, a customized method of splitting leaf into layers while doing K-means clustering, and secondly, segmenting the region of interest to blocks to help in applying statistical functions more accurately over a region.
Disease detection Mango trees Color analysis Feature extraction Segmenting Region of interest Neural network
This is a preview of subscription content, log in to check access.
Access to mango orchards over the years was granted thanks to the owner Mr. Venkatesha Rao S. R. He has an in-depth scientific understanding of challenges orchard owners face in these drought-stricken areas. In the fieldwork phase, authors were assisted by the farm manager Mr. Rajanna.
Pagola M et al (2009) New method to assess barley nitrogen nutrition status based on image colour analysis: comparison with SPAD-502. Comput Electron Agric 65(2):213–218CrossRefGoogle Scholar
Camargo A, Smith JS (2009) An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosyst Eng 102(1):9–21CrossRefGoogle Scholar
Contreras-Medina LM et al (2012) Smart sensor for real-time quantification of common symptoms present in unhealthy plants. Sensors 12(1):784–805CrossRefGoogle Scholar
Barbedo JGA (2013) Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus 2(1):660Google Scholar
Hamuda E, Glavin M, Jones E (2016) A survey of image processing techniques for plant extraction and segmentation in the field. Comput Electron Agric 125:184–199CrossRefGoogle Scholar
Wang H et al (2012) Image recognition of plant diseases based on principal component analysis and neural networks. In: 2012 8th international conference on natural computation. IEEEGoogle Scholar
Zhang S et al (2018) Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG. Optik 157:866–872CrossRefGoogle Scholar
Boese BL et al (2009) Recolonization of intertidal Zostera marina L. (eelgrass) following experimental shoot removal. J Exp Mar Biol Ecol 374(1):69–77CrossRefGoogle Scholar
Pugoy RADL, Mariano VY (2011) Automated rice leaf disease detection using color image analysis. In: Third international conference on digital image processing (ICDIP 2011), vol 8009. International Society for Optics and PhotonicsGoogle Scholar
Wiwart M et al (2009) Early diagnostics of macronutrient deficiencies in three legume species by color image analysis. Comput Electron Agric 65(1):125–132CrossRefGoogle Scholar
Camargo A, Smith JS (2009) Image pattern classification for the identification of disease causing agents in plants. Comput Electron Agric 66(2):121–125CrossRefGoogle Scholar
Lloret J et al (2011) A wireless sensor network for vineyard monitoring that uses image processing. Sensors 11(6):6165–6196CrossRefGoogle Scholar
Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318CrossRefGoogle Scholar
Macedo-Cruz A et al (2011) Digital image sensor-based assessment of the status of oat (Avena sativa L.) crops after frost damage. Sensors 11(6):6015–6036CrossRefGoogle Scholar
Patil SB, Bodhe SK (2011) Leaf disease severity measurement using image processing. Int J Eng Technol 3(5):297–301Google Scholar
Škaloudová B, Křivan V, Zemek R (2006) Computer-assisted estimation of leaf damage caused by spider mites. Comput Electron Agric 53(2):81–91CrossRefGoogle Scholar
Weizheng S et al (2008) Grading method of leaf spot disease based on image processing. In: 2008 international conference on computer science and software engineering, vol 6. IEEEGoogle Scholar