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Journal article
Multi-task learning model for agricultural pest detection from crop-plant imagery: A Bayesian approach
Computers and electronics in agriculture, Vol.218, 108719
2024
Appears in Open Access via Read & Publish Agreements
Abstract
Aphids are persistent insect pests that severely impact agricultural productivity. The detection of aphid infestations is critical for mitigating their effects. This paper presents an artificial intelligence approach to detect aphids in crop images captured by consumer-grade RGB imaging cameras. In addition to detecting the presence of aphids, the size of the aphid is an important indicator of infestation severity. To address these, we present a Bayesian multi-task learning model to detect the presence of aphids and estimate their size simultaneously.
Our model employs a joint loss function, combining a classification loss and a customised size loss. The classification component aims to identify images containing aphids, whilst the customised size loss function estimates the size of the aphids. The latter is specifically designed to account for discrepancies between the estimated and actual ground truth sizes, enhancing the accuracy of the size estimation. The model utilizes a ResNet18 backbone, ensuring robustness and adaptability across various conditions.
The proposed model was evaluated using an agricultural pest dataset consisting of images of corn, rape, rice, and wheat crops. It achieved aphid presence detection accuracies of 75.77%, 66.39%, 70.01%, and 59% for corn, rape, rice, and wheat images, respectively. An in-depth evaluation of predictive uncertainties revealed areas of high confidence and potential inaccuracies for both size and presence of aphids in images, offering insight for future model refinement. We also conducted an ablation study to thoroughly analyse the contributions of each component in proposed model.
Our model offers a valuable tool that can be used in pest management strategies for facilitating more sustainable and efficient agricultural practices.
Details
- Title
- Multi-task learning model for agricultural pest detection from crop-plant imagery: A Bayesian approach
- Authors/Creators
- Abderraouf Amrani - Murdoch UniversityDean Diepeveen - Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA 6150, AustraliaDavid Murray - School of Information Technology, Murdoch University, Murdoch, WA 6150, AustraliaMichael G.K. Jones - Murdoch UniversityFerdous Sohel - Murdoch University, Centre for Crop and Food Innovation
- Publication Details
- Computers and electronics in agriculture, Vol.218, 108719
- Publisher
- Elsevier B.V
- Identifiers
- 991005637962707891
- Copyright
- © 2024 The Author(s).
- Murdoch Affiliation
- Centre for Crop and Food Innovation; School of Information Technology
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.128 Deep Visual Recognition
- Web Of Science research areas
- Agriculture, Multidisciplinary
- Computer Science, Interdisciplinary Applications
- ESI research areas
- Computer Science