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Plant Disease Detection on Wheat Plant using Deep Learning

Patil, Sayali Sudhakar (2022) Plant Disease Detection on Wheat Plant using Deep Learning. Masters thesis, Dublin, National College of Ireland.

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Agriculture is a substantial source of revenue and a contributor to the national economy in several parts of the world. One of this industry's primary concerns is eliminating or reducing plant diseases. Diseases not only affect crop quality but also costs farmers money. Moreover, farmers occasionally need to consult professionals for advice, which is quite expensive. Therefore, to maintain crop quality and quantity, it is vital to diagnose the disease earlier to reduce pesticide use that affects the crop and the environment. The issues must be addressed by implementing innovative farming practices to aid in effective resolution. Recent advancements in machine learning techniques have significantly boosted plant disease detection studies. The current study aims to develop more precise methods for diagnosing and classifying wheat plant diseases. My experiments on the detection of plant disease using EfficientNetB0, a deep learning model show improved results in detecting plant diseases.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
S Agriculture > SB Plant culture
H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Agriculture Industry
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 28 Feb 2023 11:42
Last Modified: 01 Mar 2023 17:51

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