Climate change and competition among water users are increasingly leading to a reduction of water availability for irrigation; at the same time, traditionally non-irrigated crops require irrigation to achieve high quality standards. In the context of precision agriculture, particular attention is given to the optimization of on-farm irrigation management, based on the knowledge of within-field variability of crop and soil properties, to increase crop yield quality and ensure an efficient water use. Unmanned Aerial Vehicle (UAV) imagery is used in precision agriculture to monitor crop variability, but in the case of row-crops, image post-processing is required to separate crop rows from soil background and weeds. This study focuses on the crop row detection and extraction from images acquired through a UAV during the cropping season of 2018. Thresholding algorithms, classification algorithms, and Bayesian segmentation are tested and compared on three different crop types, namely grapevine, pear, and tomato, for analyzing the suitability of these methods with respect to the characteristics of each crop. The obtained results are promising, with overall accuracy greater than 90% and producer’s accuracy over 85% for the class “crop canopy”. The methods’ performances vary according to the crop types, input data, and parameters used. Some important outcomes can be pointed out from our study: NIR information does not give any particular added value, and RGB sensors should be preferred to identify crop rows; the presence of shadows in the inter-row distances may affect crop detection on vineyards. Finally, the best methodologies to be adopted for practical applications are discussed.

Although many studies in the literature illustrate the numerous devices and methodologies nowadays existing for assessing the spatial variability within agricultural fields, and indicate the potential for variable-rate irrigation (VRI) in vineyards, only very few works deal with the implementation of VRI systems to manage such heterogeneity, and these studies are usually conducted in experimental fields for research aims. In this study, a VR drip irrigation system was designed for a 1-ha productive vineyard in Northern Italy and managed during the agricultural season 2018, to demonstrate feasibility and effectiveness of a water supply differentiated according to the spatial variability detected in field. Electrical resistivity maps obtained by means of an electro-magnetic induction sensor were used to detect four homogeneous zones with similar soil properties. In each zone, a soil profile was opened, and soil samples were taken and analyzed in laboratory. Two irrigation management zones (MZs) were identified by grouping homogeneous zones on the basis of their hydrological properties, and an irrigation prescription map was built consistently with the total available water (TAW) content in the root zone of the two MZs. The designed drip irrigation system consisted of three independent sectors: the first two supplied water to the two MZs, while the third sector (reference sector) was managed following the farmer’s habits. During the season, irrigation in the first two sectors was fine-tuned using information provided by soil moisture probes installed in each sector. Results showed a reduction of water use by 18% compared to the ‘reference’ sector without losses in yield and product quality, and a grape’s maturation more homogeneous in time.

European rice production is concentrated in limited areas of a small number of countries. Italy is the largest European producer with over half of the total production grown on an area of 220,000 hectares, predominantly located in northern Italy. The traditional irrigation management (wet seeding and continuous flooding until few weeks before harvest—WFL) requires copious volumes of water. In order to propose effective ‘water-saving’ irrigation alternatives, there is the need to collect site-specific observational data and, at the same time, to develop agro-hydrological models to upscale field/farm experimental data to a spatial scale of interest to support water management decisions and policies. The semi-distributed modelling system developed in this work, composed of three sub-models (agricultural area, groundwater zone, and channel network), allows us to describe water fluxes dynamics in rice areas at the irrigation district scale. Once calibrated for a 1000 ha district located in northern Italy using meteorological, hydrological and land-use data of a recent four-year period (2013–2016), the model was used to provide indications on the effects of different irrigation management options on district irrigation requirements, groundwater levels and irrigation/drainage network efficiency. Four scenarios considering a complete conversion of rice irrigation management over the district were implemented: WFL; DFL—dry seeding and delayed flooding; WDA—alternate wetting and drying; WFL-W—WFL followed by post-harvest winter flooding from 15 November to 15 January. Average results for the period 2013–2016 showed that DFL and WDA would lead to a reduction in summer irrigation needs compared to WFL, but also to a postponement of the peak irrigation month to June, already characterized by a strong water demand from other crops. Finally, summer irrigation consumption for WFL-W would correspond to WFL, suggesting that the considered winter flooding period ended too early to influence summer crop water needs.

Soil electrical conductivity (EC) maps obtained through proximal soil sensing (i.e., geophysical data) are usually considered to delineate homogeneous site-specific management zones (SSMZ), used in Precision Agriculture to improve crop production. The recent literature recommends the integration of geophysical soil monitoring data with crop information acquired through multispectral (VIS-NIR) imagery. In non-flat areas, where topography can influence the soil water conditions and consequently the crop water status and the crop yield, considering topography data together with soil and crop data may improve the SSMZ delineation. The objective of this study was the fusion of EC and VIS-NIR data to delineate SSMZs in a rain-fed vineyard located in Northern Italy (Franciacorta), and the assessment of the obtained SSMZ map through the comparison with data acquired by a thermal infrared (TIR) survey carried out during a hot and dry period of the 2017 agricultural season. Data integration is performed by applying multivariate statistical methods (i.e., Principal Component Analysis). The results show that the combined use of soil, topography and crop information improves the SSMZ delineation. Indeed, the correspondence between the SSMZ map and the CWSI map derived from TIR imagery was enhanced by including the NDVI information.

In rice areas with shallow aquifers, an evaluation of alternative irrigation strategies should include the interactions between irrigation and groundwater recharge and reuse, which influence the overall irrigation efficiency. A modelling system composed of three sub-models within a MATLAB framework (a physically based, semi-distributed agro-hydrological model and two empirical models, the former for the channel network percolation and the latter for the groundwater level) was applied to a 1000 ha rice district in the Padana Plain, Italy. The calibrated framework estimates the daily time series of the water supply needed and of the groundwater level for a given irrigation management, based on the inputs provided (agro-meteorology, crop data, soil data, irrigation practices, groundwater table depth upstream of the study area). Five irrigation management strategies, relevant to the area, were compared: (i) wet seeding and continuous flooding (WFL), (ii) wet seeding and alternate wetting and drying (AWD), (iii) dry seeding and delayed flooding (DFL), (iv) dry seeding and fixed-turn irrigation FTI), (v) early dry seeding and delayed flooding (DFLearly). Due to economic advantages, dry-seeded techniques (DFL, FTI) are replacing the traditional WFL in northern Italy. Simulations show that dry seeding leads to a drastic decrease of the water table in April/May, reducing the overall irrigation efficiency of the area, and that DFL (widely adopted in the area) also causes a spike in rice irrigation needs in June when other crops increase their water demand, exposing the area to water scarcity. All the cited management strategies are assessed in the paper and AWD turned out to couple smaller irrigation needs (from June onwards) compared to continuous flooding techniques with a maintenance of the groundwater recharge, especially in the first part of the irrigation season, thus being a recommendable rice management alternative for the study area.

How accurately do Sentinel-2 (S2) images describe vine row spatial variability? Can they produce effective management zones (MZs) for precision viticulture? S2 and UAV datasets acquired over two years for different drip-irrigated vineyards in the Colli Morenici region (northern Italy) were used to assess the actual need to use UAV-NDVI maps instead of S2 images to obtain effective MZ maps. First, the correlation between S2 and UAV-NDVI values was investigated. Secondly, contingency matrices and dichotomous tables (considering UAV-MZ maps as a reference) were developed to compare MZ maps produced using S2 and UAV imagery. Moreover, data on grape production and quality were analyzed through linear discrimination analyses (LDA) to evaluate the effectiveness of S2-MZs and UAV-MZs to explain spatial variability in yield and quality data. The outcomes highlight that S2 images can be quite good tools to manage fertilization based on the within-field vigor variability, of which they capture the main features. Nevertheless, as S2-MZs with low and high vigor were over-estimated, S2-MZ maps cannot be used for high-accuracy input management. From the LDA results, the UAV-MZs appeared slightly more performant than the S2-MZs in explaining the variability in grape quality and yield, especially in the case of low-vigor MZs.