AUTOMATED PLANT DISEASE DETECTION USING DEEP LEARNING ON MOBILE PLATFORM

Vera Ruth, Rewcastle (2019) AUTOMATED PLANT DISEASE DETECTION USING DEEP LEARNING ON MOBILE PLATFORM. [Final Year Project Report] (Unpublished)

[img] PDF
AUTOMATED PLANT DISEASE DETECTION USING DEEP LEARNING ON MOBILE PLATFORM - 24 pgs.pdf

Download (11MB)
[img] PDF (Please get the password from TECHNICAL & DIGITIZATION MANAGEMENT UNIT, ext: 082-583913/ 082-583914)
AUTOMATED PLANT DISEASE DETECTION USING DEEP LEARNING ON MOBILE PLATFORM.pdf
Restricted to Registered users only

Download (46MB)

Abstract

Plant disease can affect the productivity of either hobbyist or commercial farmers. This in tum can result in loss of the crop and cause financial loss to the fanner8. The current method ofmanuaIIy recognizing a disease requires either the farmers have had experience beforehand in recognizing the type of disease or with the help of a horticulturist. This would be difficult and costly for farmers to employ a professional horticulturist or rely on knowledge gained through experience. Therefore, this project aims at developing a mobile applicatiJ:;m which is equipped with deep learning algorithm to enable the detection and identification of a disease for a particular plant. This solution provides portable platform, real time result, and without the needs of professional expert to recognize the disease. The infonnation gathered can then be compiled and shared to relevant stakeholders for record purpose, analytics and future references.

Item Type: Final Year Project Report
Additional Information: Project Report (BSc.) -- Universiti Malaysia Sarawak, 2019.
Uncontrolled Keywords: Plant disease, horticulturist, deep learning algorithm, hobbyist or commercial farmers.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Gani
Date Deposited: 18 Mar 2021 08:41
Last Modified: 18 Mar 2021 08:41
URI: http://ir.unimas.my/id/eprint/34876

Actions (For repository members only: login required)

View Item View Item