Orchard sweet cherry color distribution estimation from wireless sensor networks and video-based fruit detection

Cultivating sweet cherries is facing challenges related to the diminishing availability of critical resources, especially human labor. Recent advancements in sensors, automation, robotics, artificial intelligence, Internet of Things, and other technologies significantly impact the sweet cherry industry. These technologies are driving the transition toward more sustainable and intelligent production, improving post-harvest handling and processing of sweet cherries. The present article proposes a novel methodology for assessing the development of cherries from an agroclimatic wireless sensor network and video-based fruit detection and tracking. Climate data is collected using a few climate sensors per field, transmitted through a LoRaWAN network, and its temporal and spatial dynamics at the field level are modeled using a k-Nearest Neighbors regressor. RGB Video data is captured along rows, then fruit detection is achieved using deep learning-based methods, and fruit tracking is performed using Kalman Filters. Based on these technologies, we present two ways of assessing the maturity distribution: (i) to estimate it using video data, and (ii) to estimate it from the agroclimatic wireless sensor network data only. The methods were validated using data from five productive fields, obtaining an error rate of only 5% mean squared error in maturity estimation from agroclimatic data alone. Thus, we show that it is possible to estimate the maturity distribution solely from an agroclimatic wireless sensor network, with the system being calibrated using computer vision techniques.