Automatic lesion segmentation from rice leaf blast field images based on random forest

Mai, Xiaochun; Meng, Max Q.-H.

doi:10.1109/rcar.2016.7784035

Cited by 7 publications

(1 citation statement)

References 10 publications

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“…In order to identify the disease, the leaf images are collected, resized, color is adjusted and the simple linear iterative clustering (SLIC) is used to perform the segmentation process. Then, the regional feature is extracted and the random forest classifier is used for classification of rice leaf [8].…”

Section: Related Workmentioning

confidence: 99%

Plant Disease Detection System using Bag of Visual Words

Singh¹,

Leavline²,

Abirami³

et al. 2018

IJITCS

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Plants are important to human life since plants provide the food, shelter, rain, building material, medicine, fuel such as coal, wood, etc. Therefore, planting, growing, and protecting the plants is essential for sustainable development of any nation. The plant disease can affect the growth of the plats that is caused by pathogens, living microorganisms, bacteria, fungi, nematodes, viruses, and living agents. Hence, identifying the plant disease is very essential to protect the plants in the early stage. Moreover, the plant diseases are identified from the symptoms that appear in stem, fruit, leaf, flower, root, etc. The common symptom of the plant disease can be predicted from the appearance of leaf since the appearance of leaves highly depends on the healthiness of the plant. Therefore, this paper presents a system to identify the lesion leaf from the plants in order to detect the disease occurred in the plant. This system is developed using the bag of visual words model. Moreover, the real time images are collected for various plants and tested with this system and the system produces better results for the given set of images.

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Section: Related Workmentioning

confidence: 99%