The crop water stress index (CWSI) is one of the parameters measured in deficit irrigation and it is obtained from crop canopy temperature. However, image segmentation is required for non-leaf region exclusion in temperature measurement, as it is critical to obtain the temperature values for the calculation of the CWSI. To this end, two image-segmentation models based on support vector machine (SVM) and deep learning have been studied in this article. The models have been trained with different parameters (encoder depth, optimizer, learning rate, weight decay, validation frequency and validation patience), and several indicators (accuracy, precision, recall and F₁ score/dice coefficient), as well as prediction, training and data preparation times are discussed. The results of the F₁ score indicator are 83.11% for SVM and 86.27% for deep-learning models. More accurate results are expected for the deep-learning model by increasing the dataset, whereas the SVM model is worthwhile in terms of reduced data preparation times.

Perishable product losses can occur throughout postharvest handling. Proper monitoring of key environmental conditions during this period is essential for predicting quality losses throughout their shelf life. This paper presents the design and testing of a portable and compact datalogger for the real-time monitoring of environmental conditions throughout the food supply chain. The device developed incorporates high-precision sensors to measure temperature, relative humidity, CO₂ concentration, luminosity and vibrations, as well as wireless communication capabilities for data transmission, simplifying real-time monitoring over existing multi-component systems while keeping costs affordable. Strategies to optimize power consumption allow a month of battery life, being able to cover entire periods of transport and storage, according to the results of the autonomy test performed on the device. The datalogger uses NB-IoT and relies on other wireless communication protocols if not available to send sensor data to a cloud platform. Comparative testing with commercial dataloggers has been carried out to verify correct device measurements, and field testing has validated successful real-time data transmission along an entire refrigerated transport route. The functionality and autonomy of the proposed device meet the needs of live remote monitoring to help reduce food losses.

Fruit harvest date prediction is crucial to optimize resource management, maximize quality, and minimize waste of this food. For this purpose, it is necessary to monitor the fruit ripening stage. However, current measurement procedures pose drawbacks for widespread field deployment: laboratory trials are manual, destructive and expensive; measurements with hand-held portable equipment in the field are very time consuming; and the use of remote sensing mobile platforms has a high operating cost. In this article, a low-cost autonomous fixed sensor for continuous on-tree monitoring of pomegranates is proposed. It is based on a computer vision system able to extract reliable fruit color and size estimations automatically. In addition, an empirical quantitative and qualitative study on the effectiveness of using image-based monitoring in comparison with in situ manual and lab-based measurements for pomegranates is provided in this work. Another contribution of this article is a harvest date prediction model that employs the fruit information collected from the images. Furthermore, a thorough quantitative evaluation of the proposed prediction model for the fruit harvest date was performed, being the median error of the best model of 3.5 days.

Water scarcity in arid and semi-arid areas has led to the development of regulated deficit irrigation (RDI) strategies on most species of fruit trees in order to improve water productivity. For a successful implementation, these strategies require continuous feedback of the soil and crop water status. This feedback is provided by physical indicators from the soil–plant–atmosphere continuum, as is the case of the crop canopy temperature, which can be used for the indirect estimation of crop water stress. Infrared Radiometers (IRs) are considered as the reference tool for temperature-based water status monitoring in crops. Alternatively, in this paper, we assess the performance of a low-cost thermal sensor based on thermographic imaging technology for the same purpose. The thermal sensor was tested in field conditions by performing continuous measurements on pomegranate trees (Punica granatum L. ‘Wonderful’) and was compared with a commercial IR. A strong correlation (R² = 0.976) between the two sensors was obtained, demonstrating the suitability of the experimental thermal sensor to monitor the crop canopy temperature for irrigation management.