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- Hallösta, Simon, 1994-, et al.
(författare)
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Effects of Foreground Augmentations in Synthetic Training Data on the Use of UAVs for Weed Detection
- 2024
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Ingår i: Proceedings of Machine Learning Research. - : ML Research Press.
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Konferensbidrag (refereegranskat)abstract
- This study addresses the issue of black-grass, a herbicide-resistant weed that threatens wheat yields in Western Europe, through the use of high- resolution Unmanned Aerial Vehicles (UAVs) and synthetic data augmentation in precision agriculture. We mitigate challenges such as the need for large labeled datasets and environmental variability by employing synthetic data augmentations in training a Mask R-CNN model. Using a minimal dataset of 43 black-grass and 12 wheat field images, we achieved a 37% increase in Area Under the Curve (AUC) over the non-augmented baseline, with scaling as the most effective augmentation. The best model attained a recall of 53% at a precision of 64%, offering a promising approach for future precision agriculture applications. © NLDL 2024. All rights reserved.
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| 2. |
- Shapa, Martha, et al.
(författare)
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Mobile-based Decision Support System for Poultry Farmers : A Case of Tanzania
- 2021
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Ingår i: International Journal of Advanced Computer Sciences and Applications. - : The Science and Information Organization. - 2158-107X .- 2156-5570. ; 12:2, s. 584-590
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Tidskriftsartikel (refereegranskat)abstract
- Poultry farms in Tanzania are characterized by inadequate management practices which are mainly caused by the lack of adequate systems to guide the small-scale poultry farmers in decision making. It is well-established that information is a key factor in making effective decisions in numerous sectors including poultry farming. Furthermore, various researchers have identified the use of mobile decision support tools to be an effective way of aiding farmers in making informed decisions. In this paper, we present a mobile-based decision support system that will aid rural and small-scale poultry farmers in Tanzania to obtain reliable information that is crucial for making proper decisions in their farming activities. In this context, a mobile-based decision support system was achieved through a mobile application integrated with a chatbot assistant to provide a solution to various poultry farming-related problems and simplify their decision-making process. We used a data-driven approach towards developing an informational chatbot assistant for Android smartphones that is capable of interacting with small-scale poultry farmers through natural conversations by utilizing the RASA framework.
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