Machine Learning Based Soft Sensing Tool for the Prediction of Leaf Wetness Duration in Precision Agriculture

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.