Assessing automatic data processing algorithms for RGB-D cameras to predict fruit size and weight in apples

Data acquired using an RGB-D Azure Kinect DK camera were used to assess different automatic algorithms to estimate the size, and predict the weight of non-occluded and occluded apples. The programming of the algorithms included: (i) the extraction of images of regions of interest (ROI) using manual delimitation of bounding boxes or binary masks; (ii) estimating the lengths of the major and minor geometric axes for the purpose of apple sizing; and (iii) predicting the final weight by allometric modelling. In addition to the use of bounding boxes, the algorithms also allowed other post-mask settings (circles, ellipses and rotated rectangles) to be implemented, and different depth options (distance between the RGB-D camera and the fruits detected) for subsequent sizing through the application of the thin lens theory. Both linear and nonlinear allometric models demonstrated the ability to predict apple weight with a high degree of accuracy (R2 greater than 0.942 and RMSE < 16 g). With respect to non-occluded apples, the best weight predictions were achieved using a linear allometric model including both the major and minor axes of the apples as predictors. The mean absolute percentage error (MAPE) ranged from 5.1% to 5.7% with respective RMSE of 11.09 g and 13.02 g, depending to whether circles, ellipses, or bounding boxes were used to adjust fruit shape. The results were therefore promising and open up the possibility of implementing reliable in-field apple measurements in real time. Importantly, final weight prediction error and intermediate size estimation errors (from sizing algorithms) interact but in a way that is not easily quantifiable when weight allometric models with implicit prediction error are used. In addition, allometric models should be reviewed when applied to other apple cultivars, fruit development stages or even for different fruit growth conditions depending on canopy management.This work was partly funded by the Department of Research and Universities of the Generalitat de Catalunya (grants 2017, SGR 646 and 2021 LLAV 00088), by the Spanish Ministry of Science and Innovation / AEI/10.13039/501100011033 / ERDF (grants RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00 [PAgPROTECT project]) and by the Spanish Ministry of Science and Innovation / AEI/10.13039/501100011033 / European Union NextGeneration / PRTR (grantTED2021-131871B-I00 [DIGIFRUIT project]). We would also like to thank the Secretariat of Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya and the European Social Fund (ESF) for financing Juan Carlos Miranda’s pre-doctoral fellowship (2020 FI_B 00586). The work of Jordi Gené-Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by the European Union - NextGenerationEU.info:eu-repo/semantics/publishedVersio

Assessing ranked set sampling and ancillary data to improve fruit load estimates in peach orchards

International audienceFruit load estimation at plot level before harvest is a key issue in fruit growing. To face this challenge, two sampling methods to estimate fruit load in a peach tree orchard were compared: simple random sampling (SRS) and ranked set sampling (RSS). The study was carried out in a peach orchard (Prunus persica cv. 'Platycarpa') covering a total area of 2.24 ha. Having previously sampled the plot systematically to cover the entire area (104 individual trees or sampling points), both sampling methods (SRS and RSS) were tested by taking samples from this population with varying sample sizes from N = 4 to N = 12. Since RSS requires ancillary information to obtain the samples (ranking mechanism), several proximal and remote sensors already used or recently introduced in agriculture were assessed as data sources. A total of 14 variables provided by 5 different sensors and platforms were considered as potential ancillary variables. Among them, RGB images captured by an unmanned aerial vehicle (UAV), and used to estimate the canopy projected area of individual trees, proved to be the best of the options. This was shown by the high correlation (R = 0.85) between this area and the fruit load, providing RSS with the UAV-based canopy projected area the lowest Coefficient of Error (CE) for a given tree sample size. Then, comparing relative efficiency between random sampling (SRS) and RSS, the latter enables more precise fruit load estimates for any of the considered sample sizes. Interest and opportunity of RSS can be raised from two points of view. In terms of confidence, RSS managed to reduce the variance of fruit load estimates by about half compared to SRS. Sampling errors above the 10% threshold were always produced significantly fewer times using RSS, regardless of the sample size. In terms of operation within the plot, sample size could be reduced by 50%, from N = 10 for SRS to N = 5 for RSS, and this being expected sampling errors less than 10% in practically 70% of the samplings performed in both cases. In summary, fruit growers can take advantage of the combined use of appropriate data (RGB images from UAV) and RSS to optimize sample sizes and operational sampling costs in fruit growing

Yield sensing technologies for perennial and annual horticultural crops: a review

Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems