Sugarcane Crop Support System
Author(s):
Divij N , Sapthagiri College of Engineering; Divya K, Sapthagiri College of Engineering; Anuradha Badage, Sapthagiri College of Engineering; Ashwin Ramesh P, Sapthagiri College of Engineering; Bindhu M V, Sapthagiri College of Engineering
Keywords:
Sugarcane Disease Detection, TensorFlow Framework, Image Recognition, Transfer Learning
Abstract:
The main wealth of the India is farming. But the damaging rate of that agricultural product is mainly through the natural disasters like floods and storm or secondary factors like the virus or bacteria that infect a plant. Matching with the long diversity of conditions under which sugarcane is grown around the world, there is wide spectrum of pests and diseases which have come to acquire a place of priority for control on regional or inter-regional bases due to agro-climatic management conditions associated with the area. In addition the susceptibility of the variety to the diseases and pests aggravates the situation and creates additive problems. Traditionally, diseases in plants were identified by experienced farmers or agricultural scientists in laboratories. The latter may be an expensive process of knowing the disease infected on the plant while, the former may be inaccurate and prone to human errors. To overcome the problem, sugarcane disease detection and prevention is required. A system is proposed wherein the farmer, just by clicking a picture of plant can detect the disease and receive related solutions for the same.
Other Details:
| Manuscript Id | : | IJSTEV7I1008
|
| Published in | : | Volume : 7, Issue : 1
|
| Publication Date | : | 01/08/2020
|
| Page(s) | : | 23-27
|
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