AI Applications in Precision Agriculture: Improving Crop Management, Yield Estimation, and Environmental Sustainability

Chekuri, Ajay Varma (2024) AI Applications in Precision Agriculture: Improving Crop Management, Yield Estimation, and Environmental Sustainability. Masters thesis, Dublin, National College of Ireland.

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Abstract

The study is focused on the use of AI in precision agriculture with the aim of improving crop management, yield prediction, and sustainability. This research leverages CNNs for disease and pest classification, and ensemble models such as Gradient Boosting for yield forecasting. It addresses critical agricultural challenges. The research adopted a dataset that included over 20,000 labelled crop images to train a CNN, which achieved a validation accuracy of 53.93% in the classification of 22 categories. Results for yield prediction using the Gradient Boosting algorithm exhibited the best MAE of 0.87, with rainfall being the best single predictor.

These challenges, at least including dataset imbalance and the limited number of extracted features, were addressed with performant data augmentation, regularization techniques in model architecture, and advanced feature engineering. The results have shown that AI-driven solutions clearly enhance decision-making, ensure wise resource utilization, and give rise to sustainable farming. In establishing the ground to accessibilize scalable AI in real-world agricultural applications, it would be useful to investigate some areas that relate to enriching datasets that integrate real-time data.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Hamill, David UNSPECIFIED
Uncontrolled Keywords:	Artificial Intelligence (AI); Precision Agriculture; Yield Prediction; Sustainability
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences 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 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:	Ciara O'Brien
Date Deposited:	01 Sep 2025 15:42
Last Modified:	01 Sep 2025 15:42
URI:	https://norma.ncirl.ie/id/eprint/8686

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