Multitask Learning of Alfalfa Nutritive Value From UAV-Based Hyperspectral Images
| العنوان: | Multitask Learning of Alfalfa Nutritive Value From UAV-Based Hyperspectral Images |
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| المؤلفون: | Jessica L. Drewry, Yuchi Ma, Zhou Zhang, Parker Williams, Brian D. Luck, Qingyun Du, Yazhou Sun, Luwei Feng |
| المصدر: | IEEE Geoscience and Remote Sensing Letters. 19:1-5 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Flexibility (engineering), Computer science, business.industry, media_common.quotation_subject, Multi-task learning, Hyperspectral imaging, Geotechnical Engineering and Engineering Geology, Machine learning, computer.software_genre, Identification (information), Data quality, Production (economics), Quality (business), Profitability index, Artificial intelligence, Electrical and Electronic Engineering, business, computer, media_common |
| الوصف: | Alfalfa is a valuable and widely adapted forage crop, and its nutritive value directly affects animal performance and ultimately affects the profitability of livestock production. Traditional nutritive value measurement method is labor-intensive and time-consuming and thus hinders the determination of alfalfa nutritive values over large fields. The adoption of unmanned aerial vehicles (UAVs) facilitates the generation of images with high spatial and temporal resolutions for field-level agricultural research. Additionally, compared with other imaging modalities, hyperspectral data usually consist of hundreds of narrow spectral bands and allow the accurate detection, identification, and quantification of crop quality. Although various machine-learning methods have been developed for alfalfa quality prediction, they were all single-task models that learned independently for each quality trait and failed to utilize the underlying relatedness between each task. Inspired by the idea of multitask learning (MTL), this study aims to develop an approach that simultaneously predicts multiple quality traits. The algorithm first extracts shared information through a long short-term memory (LSTM)-based common hidden layer. To enhance the model flexibility, it is then divided into multiple branches, each containing the same or different number of task-specific fully connected hidden layers. Through comparison with multiple mainstream single-task machine-learning models, the effectiveness of the model is illustrated based on the measured alfalfa quality data and multitemporal UAV-based hyperspectral imagery. |
| تدمد: | 1558-0571 1545-598X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::c22c9bbc629a3643bed473afdba428f7 https://doi.org/10.1109/lgrs.2021.3079317 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........c22c9bbc629a3643bed473afdba428f7 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15580571 1545598X |
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