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.

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.

Advances in measurement systems and technologies are being avidly taken up in perennial tree crop research and industry applications. However, there is a lack of a standard model to support streamlined management and integration of the data generated from advanced measurement systems used in tree crop research. Furthermore, the rapid expansion in the diversity and volumes of data is increasingly highlighting the requirement for a comprehensive data model and an ecosystem for efficient orchard management and decision-making. This research focuses on the design and implementation of a novel proof-of-concept data ecosystem that enables improved data storage, management, integration, processing, analysis, and usage. Contemporary technologies proliferating in other sectors but that have had limited adoption in agricultural research have been incorporated into the model. The core of the proposed solution is a service-oriented API-driven system coupled with a standard-based digital orchard model. Applying this solution in Agriculture Victoria’s Tatura tree crop research farm (the Tatura SmartFarm) has significantly reduced overheads in research data management, enhanced analysis, and improved data resolution. This is demonstrated by the preliminary results presented for in-orchard and postharvest data collection applications. The data ecosystem developed as part of this research also establishes a foundation for early fruit traceability across industry and research.

This study aimed to evaluate the effects of fruit position, light exposure and fruit surface temperature (FST) on apple fruit colour development and fruit quality at harvest, including sunburn damage severity. This was achieved by undertaking two experiments in a high-density planting of the dark-red apple ANABP 01 in Tatura, Australia. In the 2020–2021 growing season an experiment was conducted to draw relationships between fruit position and fruit quality parameters. Here, sample fruit position and level of light exposure were respectively determined using a static LiDAR system and a portable quantum photosynthetically active radiation (PAR) sensor. At harvest the sample fruit were analysed for percentage red colour coverage, objective colour parameters (L*, a*, b*, hue angle and chroma), sunburn damage, fruit diameter (FD), soluble solids concentration (SSC), flesh firmness (FF) and starch pattern index (SPI). A second experiment was conducted in the 2021–2022 growing season and focused on how fruit shading, light exposure and the removal of ultraviolet (UV) radiation affected the FST, colour development and harvest fruit quality. Five treatments were distributed among sample fruit: fully shaded with aluminium umbrellas, shaded for one month and then exposed to sunlight until harvest, exposed for one month and then shaded until harvest, covered with a longpass UV filter and a control treatment. The development of colour in this dark-red apple cultivar was highly responsive to aspects of fruit position, and the intensity and quality of light exposure. The best-coloured fruit were exposed to higher quantities of PAR, exposed to both PAR and UV radiation simultaneously and located higher in the tree canopy. Fruit that were fully exposed to PAR and achieved better colour development also displayed higher FST and sunburn damage severity.