Machine Learning Based Soft Sensing Tool for the Prediction of Leaf Wetness Duration in Precision Agriculture
| Autor: | Sonia Bilbao, Javier Del Ser, Maria Arostegi, Diana Manjarres |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
2. Zero hunger Artificial neural network Process (engineering) Computer science business.industry 05 social sciences 050301 education Machine learning computer.software_genre 01 natural sciences GeneralLiterature_MISCELLANEOUS Variable (computer science) Data analysis Feature (machine learning) Artificial intelligence Precision agriculture Duration (project management) business 0503 education computer Leaf wetness 010606 plant biology & botany |
| Zdroj: | 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) ISBN: 9783030878689 SOCO |
| DOI: | 10.1007/978-3-030-87869-6_50 |
| Popis: | Leaf wetness often emerges as the result of the exchange of atmospheric water-soluble gases between the Earth surface and the atmosphere. The importance of this feature resides in the relationship that exists between leaf wetness and various plant diseases. In order to measure this variable, there is a need for deploying physical sensors to capture wetness readings of a crop area. However, the installation and maintenance of these sensors is a hard task that involves qualified people, time and high economical costs. Moreover, the acquisition, storage and analysis of data must be taken into consideration to infer this information and issue countermeasures preemptively. This work presents a leaf wetness soft-sensing approach that relies on predictive machine learning models to estimate the wetness of the leaves of a specific crop. Specifically, among the learning algorithms that are evaluated for this purpose, we include Random Vector Functional Link networks, a family of neural networks that embrace randomization at their core to yield a highly efficient training process. By virtue of machine learning, physical sensors can be replaced by soft-sensors capable of providing the information related to the wetness of the leaves of the crop. In this way, human effort and costs are largely reduced, while ensuring a high precision of the wetness estimation as proven by experiments with real-world data. |
| Databáze: | OpenAIRE |
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