Seasonal changes in dendrometer-derived stem variation in apple trees grown in temperate climate

Studies of daily changes in tree trunk diameter provide valuable information concerning growth patterns and their relationships with varying environmental conditions. To date, very few experiments with fruit trees evaluated the effects of climate variation on trunk shrinkage and the duration of the contraction and recovery phases and of growth. In this study, electronic dendrometers continuously monitored trunk diameter and trunk water storage dynamics of drip-irrigated ‘Gala’ apple trees (Malus x domestica Borkh.) during three growing seasons, which differed significantly in temperature, precipitation, air humidity and solar irradiation. It was found that trunk diameter and meteorological variables were closely related, even when excluding the effects of soil water limitations. During each growing season, the durations of the daily contraction phase began to increase with increasing water vapour partial pressure deficit, and decreased again in autumn, when vapour partial pressure decreased. Throughout the season, the duration of the growth phase tended to change inversely to that of both contraction and recovery phase. The relationship between maximum trunk shrinkage and vapour partial pressure was higher post than pre harvest for all years studied. The duration of contraction, recovery, and growth phases may provide valuable information concerning seasonal changes and environmental drivers of water storage dynamics in apple trees

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

In this study, the split window (SW) method was applied for land surface temperature (LST) retrieval using Landsat 8 in two apple orchards (Glindow, Altlandsberg). Four images were acquired during high demand of irrigation water from July to August 2018. After pre-processing images, the normalized difference vegetation index (NDVI) and LST were calculated by red, NIR, and thermal bands. The results were validated by interpolated infrared thermometer (IRT) measurements using the inverse distance weighting (IDW) method. In the next step, the temperature vegetation index (TVDI) was calculated based on the trapezoidal NDVI/LST space to determine the water status of apple trees in the case studies. Results show good agreement between interpolated LST using IRT measurements and remotely sensed LST calculation using SW in all satellite overpasses, where the absolute mean error was between 0.08 to 4.00 K and root mean square error (RMSE) values ranged between 0.71 and 4.23 K. The TVDI spatial distribution indicated that the trees suffered from water stress on 7 and 23 July and 8 August 2018 in Glindow apple orchard with the mean value of 0.69, 0.57, and 0.73, whereas in the Altlandsberg orchard on 17 August, the irrigation system compensated the water deficit as indicated by the TVDI value of 0.34. Moreover, a negative correlation between TVDI and vegetation water content (VWC) with correlation coefficient (r) of −0.81 was observed. The corresponding r for LST and VWC was equal to −0.89, which shows the inverse relation between water status and temperature-based indices. The results indicate that the LST and/or TVDI calculation using the proposed methods can be effectively applied for monitoring tree water status and support irrigation management in orchards using Landsat 8 satellite images without requiring ground measurements

Preface to the FRUTIC- 2019 Symposium

Fresh fruit and vegetables are the major source of essential vitamins and minerals, which are needed for human health and well-being. They are, however, perishable living products that request continuous measures for quality keeping by growers, storage operators, processors, and retailers. Sampling of fresh produce for assessing appearance, texture, flavour, and nutritional value have been established quality criteria, whereas non-invasive measurements on each individual product pre- and postharvest with traceability along the supply chain are becoming important for all role players. The FRUTIC-2019 provided a platform for researchers and practitioners to engage in technical discussions about innovations and new technologies, and explore further areas of research needed in the industry to promote quality and safety of fruit and vegetables. The first symposium of the FRUTIC series took place in Israel 1983, followed by USA, Spain, Japan, France, Germany, Italy and Chile. In 2017 and 2018 FRUTIC was organized in cooperation with FRUIT LOGISTICA in Berlin, Germany, in September 2019 FRUTIC was held in Hong Kong in cooperation with ASIA FRUIT LOGISTICA. Scientists presented their topics at the site of the ASIA FRUIT LOGISTICA trade fair at the AsiaWorld-EXPO of Hong Kong, in industry-oriented sections. Some of the selected talks are now published as full articles in this Special Issue. The FRUTIC-2019 event provided a concerted action that brought together academic scientists and the role players from fresh produce industry, to interact with each other for the purpose of information dissemination, sharing practical experience and developing road maps for the most effective way to reach the common goals. We would like to thank all participants for their contributions to the symposium program and for their contributions to this special issue. We also express our sincere thanks to the Journal of Applied Botany and Food Quality team for publishing this special issue on time

In-situ fruit analysis by means of LiDAR 3D point cloud of normalized difference vegetation index (NDVI)

A feasible method to analyse fruit at the tree is requested in precise production management. The employment of light detection and ranging (LiDAR) was approached aimed at measuring the number of fruit, quality-related size, and ripeness-related chlorophyll of fruit skin. During fruit development (65 – 130 day after full bloom, DAFB), apples were harvested and analysed in the laboratory (n = 225) with two LiDAR laser scanners measuring at 660 and 905 nm. From these two 3D point clouds, the normalized difference vegetation index (NDVILiDAR) was calculated. The correlation analysis of NDVILiDAR and chemically analysed fruit chlorophyll content showed R2 = 0.81 and RMSE = 3.63 % on the last measuring date, when fruit size reached 76 mm. The method was tested on 3D point clouds of 12 fruit trees measured directly in the orchard, during fruit growth on five measuring dates, and validated with manual fruit analysis in the orchard (n = 4632). Point clouds of individual apples were segmented from 3D point clouds of trees and fruit NDVILiDAR were calculated. The non-invasively obtained field data showed good calibration performance capturing number of fruit, fruit size, fruit NDVILiDAR, and chemically analysed chlorophyll content of R2 = 0.99, R2 = 0.98 with RMSE = 3.02 %, R2 = 0.65 with RMSE = 0.65 %, R2 = 0.78 with RMSE = 1.31 %, respectively, considering the related reference data at last measuring date 130 DAFB. The new approach of non-invasive laser scanning provided physiologically and agronomically valuable time series data on differences in fruit chlorophyll affected by the leaf area to number of fruit and leaf area to fruit fresh mass ratios. Concluding, the method provides a tool for gaining production-relevant plant data for, e.g., crop load management and selective harvesting by harvest robots

Carbon consumption of developing fruit and the fruit bearing capacity of individual RoHo 3615 and Pinova apple trees

This paper describes an approach to estimate the photosynthetic capacity and derive the optimum fruit number for each individual tree, in order to achieve a defined fruit size, which is named as the fruit bearing capacity of the tree. The estimation of fruit bearing capacity was carried out considering the total leaf area per tree as measured with a 2-D LiDAR laser scanner, LALiDAR, and key carbon-related variables of the trees including leaf gas exchange, fruit growth and respiration, in two commercial apple orchards. The range between minLALiDAR and maxLALiDAR was found to be 2.4 m on Pinova and 4.3 m on RoHo 3615 at fully developed canopy. The daily C requirement of the growing fruit and the associated leaf area demand, necessary to meet the average daily fruit C requirements showed seasonal variation, with maximum values in the middle of the growing period. The estimated fruit bearing capacity ranged from 33-95 fruit tree-1 and 45-121 fruit tree-1 on the trees of Pinova and RoHo 3615, respectively. This finding demonstrates sub-optimal crop load at harvest time in both orchards, above or below the fruit bearing capacity for individual trees. In conclusion, the LiDAR measurements of the leaf area combined with a carbon balance model allows for the estimation of fruit bearing capacity for individual trees for precise crop load management. © 2020 Polish Academy of Sciences. All rights reserved

Application of Absorption and Scattering Properties Obtained through Image Pre-Classification Method Using a Laser Backscattering Imaging System to Detect Kiwifruit Chilling Injury

Kiwifruit chilling injury (CI) damage occurs after long-term exposure to low temperature. A non-destructive approach to detect CI injury was tested in the present study, using a laser backscattering image (LBI) technique calibrated with 56 liquid phantoms for providing absorption coefficient (µa) and reduced scattering coefficient (µs’). Calibration of LBI resulted in a true-positive (TP) classification of 91.5% and 65.6% of predicted µs’ and µa, respectively. The optical properties of ‘SunGold™’and ‘Hayward’ kiwifruit were analysed at 520 nm with a two-step protocol capturing pre-classification according to the LBI parameters used in the calibration and estimation with the Farrell equation. Severely injured kiwifruit showed white corky tissue and water soaking, reduced soluble solids content and firmness measured destructively. Non-destructive classification results for ‘SunGold™’ showed a high percentage of TP for severe CI of 92% and 75% using LBI parameters directly and predicted µa and µs’ after pre-classification, respectively. The classification accuracy for severe CI ‘Hayward’ kiwifruit with LBI parameter was low (58%) and with µa and µs’ decreased further (35%), which was assumed to be due to interference caused by the long trichomes on the fruit surface

Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use

The application of visible (Vis; 400–750 nm) and near infrared red (NIR; 750–2500 nm) region spectroscopy to assess fruit and vegetables is reviewed in context of ‘point’ spectroscopy, as opposed to multi- or hyperspectral imaging. Vis spectroscopy targets colour assessment and pigment analysis, while NIR spectroscopy has been applied to assessment of macro constituents (principally water) in fresh produce in commercial practice, and a wide range of attributes in the scientific literature. This review focusses to key issues relevant to the widespread implementation of Vis-NIR technology in the fruit sector. A background to the concepts and technology involved in the use of Vis-NIR spectroscopy is provided and instrumentation for in-field and in-line applications, which has been available for two and three decades, respectively, is described. A review of scientific effort is made for the period 2015 - February 2020, in terms of the application areas, instrumentation, chemometric methods and validation procedures, and this work is critiqued through comparison to techniques in commercial use, with focus to wavelength region, optical geometry, experimental design, and validation procedures. Recommendations for future research activity in this area are made, e.g., application development with consideration of the distribution of the attribute of interest in the product and the matching of optically sampled and reference method sampled volume; instrumentation comparisons with consideration of repeatability, optimum optical geometry and wavelength range). Recommendations are also made for reporting requirements, viz. description of the application, the reference method, the composition of calibration and test populations, chemometric reporting and benchmarking to a known instrument/method, with the aim of maximising useful conclusions from the extensive work being done around the world

Estimation of daily carbon demand in sweet cherry (Prunus avium L.) production

In cherry production, the assimilate supply to the fruit is a crucial factor for growth and formation of quality parameters. The assimilate supply per fruit is limited by the relative growth capacity of trees, represented by the leaf area to fruit ratio (LA:F). In the present study, the required leaf area per fruit (LAdemand [cm² fruit-1]) of two sweet cherry cultivars, 'Bellise' and 'Regina', was estimated in 2018 and 2019, based on measured and interpolated values of fruit growth and fruit respiration rates. LAdemand changed daily with an overall increase during fruit development, showing average values in stage III in 2018 and 2019 of 139 cm² and 175 cm² in 'Bellise', while 199 cm² and 212 cm² were found in 'Regina', respectively. Estimated LAdemand for both cultivars was compared with measurements in cherries grown on girdled branches. In both years, estimated values exceeded measured values. In both years, positive correlation between LA:F and fresh mass, soluble solids content, and coloration was observed. The data obtained can be applied to evaluate the tree’s crop load for precise management

Modeling of Individual Fruit-Bearing Capacity of Trees Is Aimed at Optimizing Fruit Quality of Malus x domestica Borkh. 'Gala'

The capacity of apple trees to produce fruit of a desired diameter, i.e., fruit-bearing capacity (FBC), was investigated by considering the inter-tree variability of leaf area (LA). The LA of 996 trees in a commercial apple orchard was measured by using a terrestrial two-dimensional (2D) light detection and ranging (LiDAR) laser scanner for two consecutive years. The FBC of the trees was simulated in a carbon balance model by utilizing the LiDAR-scanned total LA of the trees, seasonal records of fruit and leaf gas exchanges, fruit growth rates, and weather data. The FBC was compared to the actual fruit size measured in a sorting line on each individual tree. The variance of FBC was similar in both years, whereas each individual tree showed different FBC in both seasons as indicated in the spatially resolved data of FBC. Considering a target mean fruit diameter of 65 mm, FBC ranged from 84 to 168 fruit per tree in 2018 and from 55 to 179 fruit per tree in 2019 depending on the total LA of the trees. The simulated FBC to produce the mean harvest fruit diameter of 65 mm and the actual number of the harvested fruit >65 mm per tree were in good agreement. Fruit quality, indicated by fruit's size and soluble solids content (SSC), showed enhanced percentages of the desired fruit quality according to the seasonally total absorbed photosynthetic energy (TAPE) of the tree per fruit. To achieve a target fruit diameter and reduce the variance in SSC at harvest, the FBC should be considered in crop load management practices. However, achieving this purpose requires annual spatial monitoring of the individual FBC of trees