Unpredictable daily climatic variations have a direct impact on fruit physiology and quality. For apples, this manifests in fruit-size deviations, whereas tomatoes often split or burst during the ripening phase, leading to a vital yield drop. Automated production monitoring can enable a precise estimation of plants’ needs and, therefore, an optimized management of natural resources. Current practices rely only on tools measuring external parameters, like determining water stress through soil moisture. However, a sensor connected to the plant itself would improve the assessment of its needs. Recently developed dendrometers, designed to be placed directly on the fruit, enable continuous measurement of its diameter. Combining the dendrometers measures with the respective climate data acquired near the monitored plants could provide valuable fruit-growing insights, such as the harvest timing or the probability of the fruit deviating from regular growth. Hence, using such insights, our work aims to develop a real-time fruit monitoring tool for growers providing recommendation services and alerts for possible risks of fruit damage. An IoT platform was developed for real-time collection, storage, and visualization of the diameter measurements and climate data. It has been designed to enable effortless and immediate deployment, requiring minimal configuration. The data transfer is done wirelessly through a LoRa network. The platform also integrates a module enabling intelligent data analysis to characterize the growing pattern and its dependency on the climate, to predict the growth evolution and the final fruit diameter, and to assess eventual deviation compared to previously established models specific for each crop. These models also consider newly acquired data to perform the prediction. Multiple acquisitions from tomatoes growing in a greenhouse and apples in orchards have been completed using the platform. The provided tool shows the potential to help optimize crop quality and production practices.
