Disease detection for potato, tomato and pepper plants using ML algorithms

Saxena, Shivani (2023) Disease detection for potato, tomato and pepper plants using ML algorithms. Masters thesis, Dublin, National College of Ireland.

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Abstract

In particular, this detailed work reviews the use of machine learning schemes for the detection of diseases of critical crops including potatoes, tomatoes, and peppers. This highly critical analysis presents the future potential of machine learning to transform conventional approaches to detecting agricultural diseases. This study points out the importance of heterogeneous and wide sets of data in successful model fitting; moreover, it discusses the difficulties of incorporating such technologies during real cultivation. These include education of farmers, infrastructure development, as well as adaptation to different weather conditions. Food security and the adoption of sustainable approaches to agricultural production is another important area reflected in this study. The paper provides perspectives on how these technologies can be integrated into current agricultural settings and stresses the need for integration between technologists, agronomists, and farmers. The study notably showcases the excellence of models such as ResNet and AlexNet, including specifically EfficientNet achieving an accuracy rating of 97.35.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Tomer, Vikas UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Agriculture Industry H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Food 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:	Ciara O'Brien
Date Deposited:	21 May 2025 10:58
Last Modified:	21 May 2025 10:58
URI:	https://norma.ncirl.ie/id/eprint/7603

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