Canopy temperature has long been recognized as an indicator of plant water status and as a potential tool for irrigation scheduling. In the present study, the potential of using thermal images for an in-field estimation of the water status of cotton under a range of irrigation regimes was investigated. Thermal images were taken with a radiometric infrared video camera. Specific leaves that appeared in the camera field of view were sampled, their LWP was measured and their temperature was calculated from the images. Regression models were built in order to predict LWP according to the crop canopy temperature and to the empirical formulation of the crop water stress index (CWSI). Statistical analysis revealed that the relationship between CWSI and LWP was more stable and had slightly higher correlation coefficients than that between canopy temperature and LWP. The regression models of LWP against CWSI and against leaf temperatures were used to create LWP maps. The classified LWP maps showed that there was spatial variability in each treatment, some of which may be attributed to the difference between sunlit and shaded leaves. The distribution of LWP in the maps showed that irrigation treatments were better distinguished from each other when the maps were calculated from CWSI than from leaf temperature alone. Furthermore, the inclusion of the spatial pattern in the classification enhanced the differences between the treatments and was better matched to irrigation amounts. Optimal determination of the water status from thermal images should be based on an overall view of the physical status as well as on the analysis of the spatial structure. Future study will involve investigating the robustness of the models and the potential of using water status maps, derived from aerial thermal images, for irrigation scheduling and variable management in commercial fields.

Spectral-based vegetation indices (VI) have been shown to be good proxies of grapevine stem water potential (Ψ_stem), assisting in irrigation decision-making for commercial vineyards. However, VI-Ψ_stem correlations are mostly reported at the leaf or canopy scales, using proximal canopy-based sensors or very-high-spatial resolution images derived from sensors mounted on small airplanes or drones. Here, for the first time, we take advantage of high-spatial resolution (3-m) near-daily images acquired from Planet’s nano-satellite constellation to derive VI-Ψ_stem correlations at the vineyard scale. Weekly Ψ_stem was measured along the growing season of 2017 in six vines each in 81 commercial vineyards and in 60 pairs of grapevines in a 2.4 ha experimental vineyard in Israel. The Clip application programming interface (API), provided by Planet, and the Google Earth Engine platform were used to derive spatially continuous time series of four VIs—GNDVI, NDVI, EVI and SAVI—in the 82 vineyards. Results show that per-week multivariable linear models using variables extracted from VI time series successfully tracked spatial variations in Ψ_stem across the experimental vineyard (Pearson’s-r = 0.45–0.84; N = 60). A simple linear regression model enabled monitoring seasonal changes in Ψ_stem along the growing season in the vineyard (r = 0.80–0.82). Planet VIs and seasonal Ψ_stem data from the 82 vineyards were used to derive a ‘global’ model for in-season monitoring of Ψ_stem at the vineyard-level (r = 0.78; RMSE = 18.5%; N = 970). The ‘global’ model, which requires only a few VI variables extracted from Planet images, may be used for real-time weekly assessment of Ψ_stem in Mediterranean vineyards, substantially improving the efficiency of conventional in-field monitoring efforts.

Sustainability in our food and fiber agriculture systems is inherently knowledge intensive. It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience. Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies between the domains of natural systems that are key to simultaneously achieve sustainability and food security. In the quest for agricultural sustainability, some high-payoff research areas are suggested to resolve critical legal and technical barriers as well as economic and social constraints. These include: the development of holistic decision-making systems, automated animal intake measurement, low-cost environmental sensors, robot obstacle avoidance, integrating remote sensing with crop and pasture models, extension methods for data-driven agriculture, methods for exploiting naturally occurring Genotype x Environment x Management experiments, innovation in business models for data sharing and data regulation reinforcing trust. Public funding for research is needed in several critical areas identified in this paper to enable sustainable agriculture and innovation.

A correct soil moisture estimation is a fundamental prerequisite for many applications: agriculture, meteorological forecast, flood and drought prediction, and, in general, water accounting and management. Traditional methods typically provide point-like measurements, but suffer from soil heterogeneity, which can produce significant misinterpretation of the hydrological scenarios. In the last decade, cosmic-ray neutron sensing (CRNS) has emerged as a promising approach for the detection of soil moisture content. CRNS can average soil moisture over a large volume (up to tens of hectares) of terrain with only one probe, thus overcoming limitations arising from the heterogeneity of the soil. The present paper introduces the development of a new CRNS instrument designed for agricultural applications and based on an innovative neutron detector. The new instrument was applied and tested in two experimental fields located in Potsdam (DE, Germany) and Lagosanto (IT, Italy). The results highlight how the new detector could be a valid alternative and robust solution for the application of the CRNS technique for soil moisture measurements in agriculture.