In:
International Journal of Recent Technology and Engineering (IJRTE), Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP, Vol. 8, No. 6 ( 2020-03-30), p. 3973-3976
Abstract:
When plants and crops are affected by pests it affects the agricultural production of the country. Agricultural productivity depends heavily on the economy. This is one of the reasons why plant disease detection plays a major role in agriculture. Usually farmers or experts observe the plants with naked eye for detection and identification of disease. But this method can be time processing, expensive and inaccurate. Detection of crop disease using a few instantaneous strategy is helpful as it decreases comprehensive surveillance job in huge crop farms and locates disease side effects quite soon, i.e. if they tend on leaves and stems. Enhanced Convolutional neural networks (ECNN) have demonstrated great performance in object recognition and image classification problems. Using a public dataset images of infected and healthy Tomato leaves collected under controlled conditions, we trained a deep convolutional neural network to identify diseases in tomato. As the result, few diseases that usually occur in tomato plants such as Late blight, Gray spot and bacterial canker are detected.
Type of Medium:
Online Resource
ISSN:
2277-3878
DOI:
10.35940/ijrte.2277-3878
DOI:
10.35940/ijrte.F8970.038620
Language:
Unknown
Publisher:
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Publication Date:
2020
detail.hit.zdb_id:
2722057-6
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