Abstract
A fully automatic methodology based on image processing is proposed to evaluate the quality of spray application sampled by water-sensitive papers (WSP). The methods proposed permit a computation of the fraction of spray coverage, an evaluation of the homogeneity of the spray spatial spread at various scales and directions, and extraction of stain and droplet size range and distribution. This allows the number of droplets per unit area and the standard droplet size spectra factors to be computed. The methods were tested with a number of test samples scanned at different resolutions, proving to be effective in situations where there is high spray coverage in the WSP, thus with considerable overlap between stains. The most suitable scanning resolution was found to be 600 dpi. The results obtained by the image processing methods were successfully compared with a manual (visual) counting of stains in a test sample.

Abstract
Precision agriculture (PA) and yield gap (Yg) analysis are promising strategies to achieve the desired sustainable intensification of agricultural production systems. Current crop Yg approaches do not consider the internal field yield variability caused by soil properties. Topographic and edaphic characteristics causing consistent high and low yield patterns in time and space can be interpreted as an ecological niche and used as proxies for potential yield (Yp) and Yg. Ecological niche models (ENMs) are statistical models originally developed to forecast a species' niche. However, its application to analyse crop yield spatio-temporal variability has never been made. This study aimed to fill this void by developing a novel approach: i) to quantify the magnitude and spatiotemporal distribution of Yp and Yg, ii) to identify the main factors that cause the Yg, and iii) to provide statistical and agronomical interpretation of the data to reduce the Yg. We performed this work using high-resolution maize yield maps from three seasons, with an ancillary dataset composed of soil electrical conductivity, soil properties and digital elevation models provided by Quinta da Cholda, Portugal. The yield maps were averaged, resulting in a standardised multiyear yield map. The 90th and 10th yield percentiles were interpreted as proxies for Yp and Yg, and analysed by an ENM machine learning algorithm - maximum entropy (MaxEnt). The average Yg and Yp were quantified as 1.5 and 19.1 ton/ha. Yp was characterised by having silty, richer soils and lower elevations, with several nutritional factors above the critical limits to maintain higher yields. Yg had loam soils coupled with higher relative elevations and lower nutrition content. This innovative modelling approach can efficiently manage high-dimensional spatio-temporal data to support advanced PA solutions, allowing detailed support for narrowing the Yg.

Abstract
Europe's 2020 strategy considers sustainable management as an increasingly important criterion for the several normative and incentives recently legislated. It is expectable that in short-term, political decision-makers and farmers will need to evaluate the type and degree of their activities' impact on the environment, economy and society. For this reason, tools are required to ease and translate these evaluations, capable of defining the main imbalance factors that need action. Despite a wide range of sustainability assessment tools available in the literature, the underlying methodologies are very similar and resort to indicators aggregated in hierarchical evaluation models that should be representative and adaptive for putative transferability. This paper provides an overview of the most relevant computational sustainability evaluation tools and their scope of application, in a benchmarking analysis. To ease the comprehension of these tools, a literature review was performed to analyse their structural elements and based on methodologies. Copyright © 2020 Inderscience Enterprises Ltd.

Abstract
Optimising the design of water distribution networks (WDNs) is a well-known problem that has been studied by numerous researchers. This work proposes a heuristic based on simulated annealing and improved by using concepts from the cross-entropy method. The proposed optimization approach is presented and used in two case studies of different complexity. The results show not only a fall in the computational effort of the new approach relative to simulated annealing but also include a comparison with other heuristic results from the literature, used to solve the same problems.

Abstract
Several vegetation indices (VI) derived from handheld spectroradiometer reflectance data in the visible spectral region were tested for modelling grapevine water status estimated by the predawn leaf water potential ((pd)). The experimental trial was carried out in a vineyard in Douro wine region, Portugal. A statistical approach was used to evaluate which VI and which combination of wavelengths per VI allows the best correlation between VIs and (pd). A linear regression was defined using a parameterization dataset. The correlation analysis between (pd) and the VIs computed with the standard formulation showed relatively poor results, with values for squared Pearson correlation coefficient (r(2)) smaller than 0.67. However, the results of r(2) highly improved for all VIs when computed with the selected best combination of wavelengths (optimal VIs). The optimal Visible Atmospherically Resistant Index (VARI) and Normalized Difference Greenness Vegetation Index (NDGI) showed the higher r(2) and stability index results. The equations obtained through the regression between measured (pd) ((pd_obs)) and optimal VARI and between (pd_obs) and optimal NDGI when using the parameterization dataset were adopted for predicting (pd) using a testing dataset. The comparison of (pd_obs) with (pd) predicted based on VARI led to R-2 = 0.79 and a regression coefficient b = 0.96. Similar R-2 was achieved for the prediction based on NDGI, but b was smaller (b = 0.93). Results obtained allow the future use of optimal VARI and NDGI for estimating (pd), supporting vineyards irrigation management.

Abstract
Non-destructive, accurate, user-friendly and low-cost approaches to determining crop leaf area (LA) are a key tool in many agronomic and physiological studies, as well as in current agricultural management. Although there are models that estimate cut rose LA in the literature, they are generally designed for a specific stage of the crop cycle, usually harvest. This study aimed to estimate the LA of cut “Red Naomi” rose stems in several phenological phases using morphological descriptors and allometric measurements derived from image processing. A statistical model was developed based on the “multiple stepwise regression” technique and considered the stem height, the number of stem leaves, and the stage of the flower bud. The model, based on 26 stems (232 leaves) collected at different developmental stages, explained 95% of the LA variance (R2 = 0.95, n = 26, p < 0.0001). The mean relative difference between the observed and the estimated LA was 8.2%. The methodology had a high accuracy and precision in the estimation of LA during crop development. It can save time, effort, and resources in determining cut rose stem LA, enhancing its application in research and production contexts. © 2016 by the authors.