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Plant-Based, Proximal and Remote Sensing in Orchards and Vineyards — State of the Art, Challenges, Data Fusion and Integration

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A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Fruit Production Systems".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 25045

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Special Issue Editors

Dr. Alessio Scalisi

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Guest Editor

Tatura SmartFarm, Agriculture Victoria, 255 Ferguson Rd., Tatura, VIC 3616, Australia
Interests: artificial intelligence; environmental physiology; fruit crops; fruit quality; irrigation; orchard sensing; plant physiology; precision horticulture; remote sensing; robotics; smart farms; spectroscopy; sustainability; traceability
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Dr. Mark Glenn O’Connell

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Guest Editor

Tatura SmartFarm, Agriculture Victoria, 255 Ferguson Rd, Tatura, VIC 3616, Australia
Interests: agronomy; irrigation science; crop physiology; fruit crops; fruit quality; orchard sensing; precision horticulture; remote sensing; robotics; smart farms; spectroscopy; sustainability; traceability

Dr. Ian Goodwin

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Guest Editor

Tatura SmartFarm, Agriculture Victoria, 255 Ferguson Rd, Tatura, VIC 3616, Australia
Interests: light; plant; fruit; pome; spring phenology; irrigation science; orchard management; orchard sensing; plant physiology; precision horticulture

Special Issue Information

Dear Colleagues,

Orchard and vineyard management is rapidly changing as we navigate a fast-paced revolution often referred to as Agriculture 4.0. This revolution is leading to increased automation that requires the use of plant-based, proximal and remote sensors to collect Big Data in orchards and vineyards. Big Data can support fruit, vegetable and nut production in facing global and modern challenges such as the increasing population, climate change, water scarcity, food waste, biosecurity and lack of traceability and credence.

Plant-based or contact sensing (e.g., trunk and fruit dendrometry, near-infrared and fluorescence spectroscopy) obtains the most accurate information on plants’ physiological responses to biotic and abiotic stress at a tree level and on a continuous time scale. Proximal and remote sensing (e.g., machine vision, LiDAR, multispectral and hyperspectral) from ground or aerial platforms and satellites allows for the collection of larger datasets that can provide more detailed spatial information across orchard blocks. Data fusion and integration from different plant-based, proximal and remote sensors and/or data sources remains a practical challenge, but successful attempts can provide the most consistent and accurate data and information about orchards and vineyards.

This Special Issue aims to collect state-of-the-art research on innovative plant-based, proximal and remote sensors used to collect data in orchards and vineyards and on their data fusion and integration to inform orchard management decisions.

Dr. Alessio Scalisi
Dr. Mark Glenn O’Connell
Dr. Ian Goodwin
Guest Editors

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Keywords

artificial intelligence
big data
data integration
data fusion
fruit crops
orchard automation
orchard management
precision horticulture
robotics
smart farms

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Published Papers (9 papers)

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14 pages, 2596 KiB

Open AccessArticle

Occurrence of Wetness on the Fruit Surface Modeled Using Spatio-Temporal Temperature Data from Sweet Cherry Tree Canopies

by Nicolas Tapia-Zapata, Andreas Winkler and Manuela Zude-Sasse

Horticulturae 2024, 10(7), 757; https://doi.org/10.3390/horticulturae10070757 - 17 Jul 2024

Viewed by 1303

Abstract

Typically, fruit cracking in sweet cherry is associated with the occurrence of free water at the fruit surface level due to direct (rain and fog) and indirect (cold exposure and dew) mechanisms. Recent advances in close range remote sensing have enabled the monitoring of the temperature distribution with high spatial resolution based on light detection and ranging (LiDAR) and thermal imaging. The fusion of LiDAR-derived geometric 3D point clouds and merged thermal data provides spatially resolved temperature data at the fruit level as LiDAR 4D point clouds. This paper aimed to investigate the thermal behavior of sweet cherry canopies using this new method with emphasis on the surface temperature of fruit around the dew point. Sweet cherry trees were stored in a cold chamber (6 °C) and subsequently scanned at different time intervals at room temperature. A total of 62 sweet cherry LiDAR 4D point clouds were identified. The estimated temperature distribution was validated by means of manual reference readings (n = 40), where average

R^{2}

values of 0.70 and 0.94 were found for ideal and real scenarios, respectively. The canopy density was estimated using the ratio of the number of LiDAR points of fruit related to the canopy. The occurrence of wetness on the surface of sweet cherry was visually assessed and compared to an estimated dew point (

Y_{d e w}

) index. At mean

Y_{d e w}

of 1.17, no wetness was observed on the fruit surface. The canopy density ratio had a marginal impact on the thermal kinetics and the occurrence of wetness on the surface of sweet cherry in the slender spindle tree architecture. The modelling of fruit surface wetness based on estimated fruit temperature distribution can support ecophysiological studies on tree architectures considering resilience against climate change and in studies on physiological disorders of fruit. Full article

(This article belongs to the Special Issue Plant-Based, Proximal and Remote Sensing in Orchards and Vineyards — State of the Art, Challenges, Data Fusion and Integration)

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18 pages, 2675 KiB

Open AccessEditor’s ChoiceArticle

A Data Ecosystem for Orchard Research and Early Fruit Traceability

by Stephen Ross Williams, Arbind Agrahari Baniya, Muhammad Sirajul Islam and Kieran Murphy

Horticulturae 2023, 9(9), 1013; https://doi.org/10.3390/horticulturae9091013 - 8 Sep 2023

Cited by 5 | Viewed by 2056

Abstract

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. Full article

(This article belongs to the Special Issue Plant-Based, Proximal and Remote Sensing in Orchards and Vineyards — State of the Art, Challenges, Data Fusion and Integration)

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16 pages, 5555 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of Computer Vision Systems and Applications to Estimate Trunk Cross-Sectional Area, Flower Cluster Number, Thinning Efficacy and Yield of Apple

by Luis Gonzalez Nieto, Anna Wallis, Jon Clements, Mario Miranda Sazo, Craig Kahlke, Thomas M. Kon and Terence L. Robinson

Horticulturae 2023, 9(8), 880; https://doi.org/10.3390/horticulturae9080880 - 3 Aug 2023

Cited by 5 | Viewed by 2219

Abstract

Precision crop load management of apple requires counting fruiting structures at various times during the year to guide management decisions. The objective of the current study was to evaluate the accuracy of and compare different commercial computer vision systems and computer applications to estimate trunk cross-sectional area (TCSA), flower cluster number, thinning efficacy, and yield estimation. These studies evaluated two companies that offer different vision systems in a series of trials across 23 orchards in four states. Orchard Robotics uses a proprietary camera system, and Pometa (previously Farm Vision) uses a cell phone camera system. The cultivars used in the trials were ‘NY1’, ‘NY2’, ‘Empire’, ‘Granny Smith’, ‘Gala’, ‘Fuji’, and ‘Honeycrisp’. TCSA and flowering were evaluated with the Orchard Robotics camera in full rows. Flowering, fruit set, and yield estimation were evaluated with Pometa. Both systems were compared with manual measurements. Our results showed a positive linear correlation between the TCSA with the Orchard Robotics vision system and manual measurements, but the vision system underestimated the TCSA in comparison with the manual measurements (R²s between 0.5 and 0.79). Both vision systems showed a positive linear correlation between nubers of flowers and manual counts (R²s between 0.5 and 0.95). Thinning efficacy predictions (in June) were evaluated using the fruit growth rate model, by comparing manual measurements and the MaluSim computer app with the computer vision system of Pometa. Both systems showed accurate predictions when the numbers of fruits at harvest were lower than 200 fruit/tree, but our results suggest that, when the numbers of fruits at harvest were higher than 200 fruit/tree, both methods overestimated final fruit numbers per tree when compared with final fruit numbers at harvest (R²s 0.67 with both systems). Yield estimation was evaluated just before harvest (August) with the Pometa system. Yield estimation was accurate when fruit numbers were fewer than 75 fruit per tree, but, when the numbers of fruit at harvest were higher than 75 fruit per tree, the Pometa vision system underestimated the final yield (R² = 0.67). Our results concluded that the Pometa system using a smartphone offered advantages such as low cost, quick access, simple operation, and accurate precision. The Orchard Robotics vision system with an advanced camera system provided more detailed and accurate information in terms of geo-referenced information for individual trees. Both vision systems evaluated are still in early development and have the potential to provide important information for orchard managers to improve crop load management decisions. Full article

(This article belongs to the Special Issue Plant-Based, Proximal and Remote Sensing in Orchards and Vineyards — State of the Art, Challenges, Data Fusion and Integration)

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14 pages, 3279 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

In-Orchard Sizing of Mango Fruit: 2. Forward Estimation of Size at Harvest

by Marcelo H. Amaral and Kerry B. Walsh

Horticulturae 2023, 9(1), 54; https://doi.org/10.3390/horticulturae9010054 - 3 Jan 2023

Cited by 13 | Viewed by 2953

Abstract

Forecast of tree fruit yield requires prediction of harvest time fruit size as well as fruit number. Mango (Mangifera indica L.) fruit mass can be estimated from correlation to measurements of fruit length (L), width (W) and thickness (T). On-tree measurements of individually tagged fruit were undertaken using callipers at weekly intervals until the fruit were past commercial maturity, as judged using growing degree days (GDD), for mango cultivars ‘Honey Gold’, ‘Calypso’ and ‘Keitt’ at four locations in Australia and Brazil during the 2020/21 and 21/22 production seasons. Across all cultivars, the linear correlation of fruit mass to LWT was characterized by a R² of 0.99, RMSE of 29.9 g and slope of 0.5472 g/cm³, while the linear correlation of fruit mass to

L (\frac{(W + T)}{2}

)², mimicking what can be measured by machine vision of fruit on tree, was characterized by a R² of 0.97, RMSE of 25.0 g and slope of 0.5439 g/cm³. A procedure was established for the prediction of fruit size at harvest based on measurements made five and four or four and three weeks prior to harvest (approx. 514 and 422 GDD, before harvest, respectively). Linear regression models on weekly increase in fruit mass estimated from lineal measurements were characterized by an R² > 0.88 for all populations, with an average slope (rate of increase) of 19.6 ± 7.1 g/week, depending on cultivar, season and site. The mean absolute percentage error for predicted mass compared to harvested fruit weight for estimates based on measurements of the earlier and later intervals was 16.3 ± 1.3% and 4.5 ± 2.4%, respectively. Measurement at the later interval allowed better accuracy on prediction of fruit tray size distribution. A recommendation was made for forecast of fruit mass at harvest based on in-field measurements at approximately 400 to 450 GDD units before harvest GDD and one week later. Full article

(This article belongs to the Special Issue Plant-Based, Proximal and Remote Sensing in Orchards and Vineyards — State of the Art, Challenges, Data Fusion and Integration)

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17 pages, 4835 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

In-Orchard Sizing of Mango Fruit: 1. Comparison of Machine Vision Based Methods for On-The-Go Estimation

by Chiranjivi Neupane, Anand Koirala and Kerry B. Walsh

Horticulturae 2022, 8(12), 1223; https://doi.org/10.3390/horticulturae8121223 - 19 Dec 2022

Cited by 19 | Viewed by 2869

Abstract

Estimation of fruit size on-tree is useful for yield estimation, harvest timing and market planning. Automation of measurement of fruit size on-tree is possible using RGB-depth (RGB-D) cameras, if partly occluded fruit can be removed from consideration. An RGB-D Time of Flight camera was used in an imaging system that can be driven through an orchard. Three approaches were compared, being: (i) refined bounding box dimensions of a YOLO object detector; (ii) bounding box dimensions of an instance segmentation model (Mask R-CNN) applied to canopy images, and (iii) instance segmentation applied to extracted bounding boxes from a YOLO detection model. YOLO versions 3, 4 and 7 and their tiny variants were compared to an in-house variant, MangoYOLO, for this application, with YOLO v4-tiny adopted. Criteria developed to exclude occluded fruit by filtering based on depth, mask size, ellipse to mask area ratio and difference between refined bounding box height and ellipse major axis. The lowest root mean square error (RMSE) of 4.7 mm and 5.1 mm on the lineal length dimensions of a population (n = 104) of Honey Gold and Keitt varieties of mango fruit, respectively, and the lowest fruit exclusion rate was achieved using method (ii), while the RMSE on estimated fruit weight was 113 g on a population weight range between 180 and 1130 g. An example use is provided, with the method applied to video of an orchard row to produce a weight frequency distribution related to packing tray size. Full article

(This article belongs to the Special Issue Plant-Based, Proximal and Remote Sensing in Orchards and Vineyards — State of the Art, Challenges, Data Fusion and Integration)

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26 pages, 5498 KiB

Open AccessArticle

Continuous Third Phase Fruit Monitoring in Olive with Regulated Deficit Irrigation to Set a Quantitative Index of Water Stress

by Arash Khosravi, Matteo Zucchini, Adriano Mancini and Davide Neri

Horticulturae 2022, 8(12), 1221; https://doi.org/10.3390/horticulturae8121221 - 19 Dec 2022

Cited by 7 | Viewed by 2181

Abstract

The transversal fruit diameter (FD) was monitored continuously by automatic extensimeters (fruit gauges) in order to monitor fruit growth dynamics under deficit irrigation treatments. The daily diameter fluctuation (ΔD, mm), the daily growth (ΔG, mm), the cumulative fruit growth (CFG, mm), and the fruit relative growth rate (RGR, mm mm⁻¹ h⁻¹) of four olive cultivars (Ascolana dura, Piantone di Falerone, Arbequina, and Lea) were studied during the third phase of fruit growth. Two regulated deficit irrigation treatments DI-20 (20% of ET_c) and DI-10 (10% of ET_c) were applied. The daily hysteretic pattern of FD versus the environmental variable of vapor pressure deficit (VPD) was evaluated using the data of a local weather station. The assessment of fruit growth parameters showed cultivar-specific response to water stress. For instance, after performing deficit irrigation, minimum RGR in different cultivars downsized with various slopes which suggested a very different response of the cultivars to dehydration. On the other hand, the daily hysteretic pattern of FD versus VPD was detected in all the studied cultivars, and a quantitative index (height of hysteresis curves) used for explanation of hysteresis magnitude’s changed according to the deficit irrigation treatments. The results showed a significant reduction of height of hysteresis curves by irrigation treatments which were not cultivar-specific. The quantitative index for hysteresis curve magnitude’s change in the four olive cultivars of Ascolana dura, Piantone di Falerone, Arbequina and Lea can efficiently estimate the plant water response to irrigation treatment in olive orchards. However, further investigation needs to be done to implement precise irrigation systems. Full article

(This article belongs to the Special Issue Plant-Based, Proximal and Remote Sensing in Orchards and Vineyards — State of the Art, Challenges, Data Fusion and Integration)

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15 pages, 3399 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Monitoring Stem Water Potential with an Embedded Microtensiometer to Inform Irrigation Scheduling in Fruit Crops

by Alan N. Lakso, Michael Santiago and Abraham D. Stroock

Horticulturae 2022, 8(12), 1207; https://doi.org/10.3390/horticulturae8121207 - 16 Dec 2022

Cited by 23 | Viewed by 4143

Abstract

The water status of fruit and nut crops is critical to the high productivity, quality and value of these crops. Water status is often estimated and managed with indirect measurements of soil moisture and models of evapotranspiration. However, cultivated trees and vines have characteristics and associated cultural practices that complicate such methods, particularly variable discontinuous canopies, and extensive but low-density, variable root systems with relatively high hydraulic resistance. Direct and continuous measurement of plant water status is desirable in these crops as the plant integrates its unique combination of weather, soil and cultural factors. To measure plant water potential with high temporal sampling rates, a stem-embedded microchip microtensiometer sensor has been developed and tested in several fruit crops for long-term continuous monitoring of stem water potential. Results on several fruit crops in orchards and vineyards have been good to excellent, with very good correlations to the pressure chamber standard method. The primary challenge has been establishing and maintaining the intimate contact with the xylem for long periods of time, with variable stem anatomies, stem growth and wound reactions. Sources of variability in the measurements and utilization of the continuous data stream, in relation to irrigation scheduling, are discussed. Direct continuous and long-term field measurements are possible and provide unique opportunities for both research and farming. Full article

(This article belongs to the Special Issue Plant-Based, Proximal and Remote Sensing in Orchards and Vineyards — State of the Art, Challenges, Data Fusion and Integration)

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15 pages, 6353 KiB

Open AccessArticle

Lightweight Apple Detection in Complex Orchards Using YOLOV5-PRE

by Lijuan Sun, Guangrui Hu, Chao Chen, Haoxuan Cai, Chuanlin Li, Shixia Zhang and Jun Chen

Horticulturae 2022, 8(12), 1169; https://doi.org/10.3390/horticulturae8121169 - 8 Dec 2022

Cited by 25 | Viewed by 2981

Abstract

The detection of apple yield in complex orchards plays an important role in smart agriculture. Due to the large number of fruit trees in the orchard, improving the speed of apple detection has become one of the challenges of apple yield detection. Additional challenges in the detection of apples in complex orchard environments are vision obstruction by leaves, branches and other fruit, and uneven illumination. The YOLOv5 (You Only Look Once version 5) network structure has thus far been increasingly utilized for fruit recognition, but its detection accuracy and real-time detection speed can be improved. Thus, an upgraded lightweight apple detection method YOLOv5-PRE (YOLOv5 Prediction) is proposed for the rapid detection of apple yield in an orchard environment. The ShuffleNet and the GhostNet lightweight structures were introduced into the YOLOv5-PRE model to reduce the size of the model, and the CA (Coordinate Attention) and CBAM (Convolutional Block Attention Module) attention mechanisms were used to improve the detection accuracy of the algorithm. After applying this algorithm on PC with NVIDIA Quadro P620 GPU, and after comparing the results of the YOLOv5s (You Only Look Once version 5 small) and the YOLOv5-PRE models outputs, the following conclusions were obtained: the average precision of the YOLOv5-PRE model was 94.03%, which is 0.58% higher than YOLOv5s. As for the average detection time of a single image on GPU and CPU, it was 27.0 ms and 172.3 ms, respectively, which is 17.93% and 35.23% higher than YOLOV5s. Added to that, the YOLOv5-PRE model had a missed detection rate of 6.54% when being subject to back-light conditions, and a false detection rate of 4.31% when facing front-light conditions, which are 2.8% and 0.86% higher than YOLOv5s, respectively. Finally, the feature extraction process of the YOLOv5-PRE model was presented in the form of a feature map visualization, which enhances the interpretability of the model. Thus, the YOLOv5-PRE model is more suitable for transplanting into embedded devices and adapts well to different lighting conditions in the orchard, which provides an effective method and a theoretical basis for the rapid detection of apples in the process of rapid detection of apple yield. Full article

(This article belongs to the Special Issue Plant-Based, Proximal and Remote Sensing in Orchards and Vineyards — State of the Art, Challenges, Data Fusion and Integration)

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9 pages, 587 KiB

Open AccessBrief Report

Correlation of the Grapevine (Vitis vinifera L.) Leaf Chlorophyll Concentration with RGB Color Indices

by Péter Bodor-Pesti, Dóra Taranyi, Diána Ágnes Nyitrainé Sárdy, Lien Le Phuong Nguyen and László Baranyai

Horticulturae 2023, 9(8), 899; https://doi.org/10.3390/horticulturae9080899 - 7 Aug 2023

Cited by 8 | Viewed by 2118

Abstract

Spectral investigation of the canopy has an increasing importance in precision viticulture to monitor the effect of biotic and abiotic stress factors. In this study, RGB (color model, red, green, blue)-based vegetation indices were evaluated to find a correlation with grapevine leaf chlorophyll concentration. ‘Hárslevelű’ (Vitis vinifera L.) leaf samples were obtained from a commercial vineyard and digitalized. The chlorophyll concentration of the samples was determined with a portable chlorophyll meter. Image processing and color analyses were performed to determine the RGB average values of the digitized samples. According to the RGB values, 31 vegetation indices were calculated and evaluated with a correlation test and multivariate regression. The Pearson correlation between the chlorophyll concentration and most of the indices was significant (p < 0.01), with some exceptions. The same results were obtained with the Spearman correlation as the relationship had high significance (p < 0.01) for most of the indices. The highest Pearson correlation was obtained with the index PCA2 (Principal Component Analysis 2), while Spearman correlation was the highest for RMB (difference between red and blue) and GMB (difference between green and blue). The multivariate regression model also showed a high correlation with the pigmentation. We consider that our results would be applicable in the future to receive information about the canopy physiological status monitored with on-the-go sensors. Full article

(This article belongs to the Special Issue Plant-Based, Proximal and Remote Sensing in Orchards and Vineyards — State of the Art, Challenges, Data Fusion and Integration)

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