Computer Vision Based Approach for Detection of Disease in Cotton Plants

Mendonca, Steve (2022) Computer Vision Based Approach for Detection of Disease in Cotton Plants. Masters thesis, Dublin, National College of Ireland.

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Abstract

Agriculture is one of the most important sectors in the well-being and survival of a country. This sector needs to be protected and catered for as it also increases the cash flow in the country. Cotton is one such important cash crop which needs to be taken care of. Sometimes fields of cotton are susceptible to diseases. These diseases can cause big problems as they spread among the plants rendering them useless. It is important to identify and take out these diseased plants early on before the damage is irreparable. This paper works around building a CNN model with multiple layers to identify and detect these diseased plants efficiently so that the concerned people could take action against them. The dataset used had 2310 images which was later augmented to avoid the problem of overfitting. The CNN model could identify the diseases with an accuracy of 96%. This model was compared with two pre-trained models, VGG16 and DenseNet121. The CNN model outperformed the two on certain important metrics showing that with further work done, it could be used on a higher scale and with better efficiency.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Yaqoob, Abid UNSPECIFIED
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 Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	19 May 2023 16:53
Last Modified:	19 May 2023 16:53
URI:	https://norma.ncirl.ie/id/eprint/6617

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