An Automated Machine Learning Framework in Unmanned Aircraft Systems: New Insights into Agricultural Management Practices Recognition Approaches
| Autor: | Niall Burnside, Ants Vain, Raul Sampaio de Lima, Bruno Rucy Carneiro Alves de Lima, Ming-Der Yang, Kalev Sepp, Miguel Villoslada Peciña, Victor Henrique Cabral Pinheiro, Kai-Yun Li, K. Sepp |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
Science Data classification Automatic identification and data capture Machine learning computer.software_genre agriculture decision-making variety performance trials automated machine learning precision agriculture Artificial neural network Contextual image classification business.industry Random forest agricultural management practices crop phenotyping Support vector machine Hyperparameter optimization General Earth and Planetary Sciences unmanned aircraft system crop breeding Artificial intelligence Precision agriculture business computer image classification |
| Zdroj: | Remote Sensing Volume 13 Issue 16 Pages: 3190 Remote Sensing, Vol 13, Iss 3190, p 3190 (2021) |
| ISSN: | 2072-4292 |
| Popis: | The recent trend of automated machine learning (AutoML) has been driving further significant technological innovation in the application of artificial intelligence from its automated algorithm selection and hyperparameter optimization of the deployable pipeline model for unraveling substance problems. However, a current knowledge gap lies in the integration of AutoML technology and unmanned aircraft systems (UAS) within image-based data classification tasks. Therefore, we employed a state-of-the-art (SOTA) and completely open-source AutoML framework, Auto-sklearn, which was constructed based on one of the most widely used ML systems: Scikit-learn. It was combined with two novel AutoML visualization tools to focus particularly on the recognition and adoption of UAS-derived multispectral vegetation indices (VI) data across a diverse range of agricultural management practices (AMP). These include soil tillage methods (STM), cultivation methods (CM), and manure application (MA), and are under the four-crop combination fields (i.e., red clover-grass mixture, spring wheat, pea-oat mixture, and spring barley). Furthermore, they have currently not been efficiently examined and accessible parameters in UAS applications are absent for them. We conducted the comparison of AutoML performance using three other common machine learning classifiers, namely Random Forest (RF), support vector machine (SVM), and artificial neural network (ANN). The results showed AutoML achieved the highest overall classification accuracy numbers after 1200 s of calculation. RF yielded the second-best classification accuracy, and SVM and ANN were revealed to be less capable among some of the given datasets. Regarding the classification of AMPs, the best recognized period for data capture occurred in the crop vegetative growth stage (in May). The results demonstrated that CM yielded the best performance in terms of classification, followed by MA and STM. Our framework presents new insights into plant–environment interactions with capable classification capabilities. It further illustrated the automatic system would become an important tool in furthering the understanding for future sustainable smart farming and field-based crop phenotyping research across a diverse range of agricultural environmental assessment and management applications. |
| Databáze: | OpenAIRE |
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