An innovative implementation of an electronic leaf wetness sensor (LWS) is proposed. It utilizes capacitive sensing, combined with an innovative data acquisition method, which implements a capacitance-to-digital converter. The study explores the design procedure of a capacitive LWS, proposing an analytical approach and emphasizing low manufacturing costs. Since the LWS is intended for Internet-of-Things applications, this article estimates its energy consumption, introducing a boost regulator to optimize power usage, contributing to extend the battery life. The study presents simulation results and experimental validations, including an ad-hoc calibration procedure under controlled conditions. The sensors were tested in real agricultural environments over a complete vegetative season, demonstrating their capability to operate continuously without problems.

The paper presents a double-radio wireless multimedia sensor node (WMSN) with a camera on board, designed for plant proximal monitoring. Camera sensor nodes represent an effective solution to monitor the crop at the leaf or fruit scale, with details that cannot be retrieved with the same precision through satellites or unnamed aerial vehicles (UAVs). From the technological point of view, WMSNs are characterized by very different requirements, compared to standard wireless sensor nodes; in particular, the network data rate results in higher energy consumption and incompatibility with the usage of battery-powered devices. Avoiding energy harvesters allows for device miniaturization and, consequently, application flexibility, even for small plants. To do this, the proposed node has been implemented with two radios, with different roles. A GPRS modem has been exclusively implemented for image transmission, while all other tasks, including node monitoring and camera control, are performed by a LoRaWAN class A end-node that connects every 10 min. Via the LoRaWAN downlink, it is possible to efficiently control the camera settings; the shooting times and periodicity, according to weather conditions; the eventual farming operations; the crop growth stages and the season. The node energy consumption has been verified in the laboratory and in the field, showing that it is possible to acquire one picture per day for more than eight months without any energy harvester, opening up further possible implementations for disease detection and production optimization.