Fruit skin colour is one of the most important visual fruit quality parameters driving consumer preferences. Proximal sensors such as machine vision cameras can be used to detect skin colour in fruit visible in collected images, but their accuracy in variable orchard light conditions remains a practical challenge. This work aimed to derive a new fruit skin colour attribute—namely a Colour Development Index (CDI), ranging from 0 to 1, that intuitively increases as fruit becomes redder—to assess colour development in peach and nectarine fruit skin. CDI measurements were generated from high-resolution images collected on both east and west sides of the canopies of three peach and one nectarine cultivars using the commercial mobile platform Cartographer (Green Atlas). Fruit colour (RGB values) was extracted from the central pixels of detected fruit and converted into a CDI. The repeatability of CDI measurements under different light environments was tested by scanning orchards at different times of the day. The effects of cultivar and canopy side on CDI were also determined. CDI data was related to the index of absorbance difference (I_AD)—an index of chlorophyll degradation that was correlated with ethylene emission—and its response to time from harvest was modelled. The CDI was only significantly altered when measurements were taken in the middle of the morning or in the middle of the afternoon, when the presence of the sun in the image caused significant alteration of the image brightness. The CDI was tightly related to I_AD, and CDI values plateaued (0.833 ± 0.009) at I_AD ≤ 1.20 (climacteric onset) in ‘Majestic Pearl’ nectarine, suggesting that CDI thresholds show potential to be used for harvest time decisions and to support logistics. In order to obtain comparable CDI datasets to study colour development or forecast harvest time, it is recommended to scan peach and nectarine orchards at night, in the early morning, solar noon, or late afternoon. This study found that the CDI can serve as a standardised and objective skin colour index for peaches and nectarines.

A comprehensive characterization of water stress is needed for the development of automated irrigation protocols aiming to increase olive orchard environmental and economical sustainability. The main aim of this study is to determine whether a combination of continuous leaf turgor, fruit growth, and sap flow responses improves the detection of mild water stress in two olive cultivars characterized by different responses to water stress. The sensitivity of the tested indicators to mild stress depended on the main mechanisms that each cultivar uses to cope with water deficit. One cultivar showed pronounced day to day changes in leaf turgor and fruit relative growth rate in response to water withholding. The other cultivar reduced daily sap flows and showed a pronounced tendency to reach very low values of leaf turgor. Based on these responses, the sensitivity of the selected indicators is discussed in relation to drought response mechanisms, such as stomatal closure, osmotic adjustment, and tissue elasticity. The analysis of the daily dynamics of the monitored parameters highlights the limitation of using non-continuous measurements in drought stress studies, suggesting that the time of the day when data is collected has a great influence on the results and consequent interpretations, particularly when different genotypes are compared. Overall, the results highlight the need to tailor plant-based water management protocols on genotype-specific physiological responses to water deficit and encourage the use of combinations of plant-based continuously monitoring sensors to establish a solid base for irrigation management.

Automatic in-field fruit recognition techniques can be used to estimate fruit number, fruit size, fruit skin color, and yield in fruit crops. Fruit color and size represent two of the most important fruit quality parameters in stone fruit ( Prunus sp.). This study aimed to evaluate the reliability of a commercial mobile platform, sensors, and artificial intelligence software system for fast estimates of fruit number, fruit size, and fruit skin color in peach ( Prunus persica ), nectarine ( P. persica var. nucipersica ), plum ( Prunus salicina ), and apricot ( Prunus armeniaca ), and to assess their spatial and temporal variability. An initial calibration was needed to obtain estimates of absolute fruit number per tree and a forecasted yield. However, the technology can also be used to produce fast relative density maps in stone fruit orchards. Fruit number prediction accuracy was ≥90% in all the crops and training systems under study. Overall, predictions of fruit number in two-dimensional training systems were slightly more accurate. Estimates of fruit diameter (FD) and color did not need an initial calibration. The FD predictions had percent standard errors <10% and root mean square error <5 mm under different training systems, row spacing, crops, and fruit position within the canopy. Hue angle, a color attribute previously associated with fruit maturity in peach and nectarine, was the color attribute that was best predicted by the mobile platform. A new color parameter—color development index (CDI), ranging from 0 to 1—was derived from hue angle. The adoption of CDI, which represents the color progression or distance from green, improved the interpretation of color measurements by end-users as opposed to hue angle and generated more robust color estimations in fruit that turn purple when ripe, such as dark plum. Spatial maps of fruit number, FD, and CDI obtained with the mobile platform can be used to inform orchard decisions such as thinning, pruning, spraying, and harvest timing. The importance and application of crop yield and fruit quality real-time assessments and forecasts are discussed.