Special Issue "In-Field Estimation of Fruit Quality and Quantity"

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Agricultural Engineering".

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editor

Prof. Dr. Kerry Brian Walsh
Website
Guest Editor
Centre for Plant and Water Science, Central Queensland University,Rockhampton, Queensland 4702, Australia
Interests: horticulture; plant physiology; near infrared spectroscopy; fruit tree crop quantity and quality estimation; plant nutrition; fruit quality

Special Issue Information

Dear colleagues,

Advances in machine vision, image processing, spectroscopy and other technologies are allowing in field assessment of various fruit crop attributes. This includes estimation of optimum harvest timing through use of remote temperature monitoring technologies, automated assessment of flowering stage and level, near infra red spectroscopic assessment of fruit attributes, fruit detection, counting and sizing.  Effectively there is a shift in technology from use in the controlled environment of the packhouse to use in the uncontrolled field environment of the orchard.  These tools can inform farm management decisions on crop agronomy, harvest timing, harvest resourcing (labour and materials) and marketing. A call is open for original papers which address the development or application of such technologies in the application of fruit quality and quantity, with extension to the use of these technologies in automation of fruit harvest.

Prof. Dr. Kerry Brian Walsh
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Open AccessArticle
Manipulation of Fruit Dry Matter via Seasonal Pruning and Its Relationship to d’Anjou Pear Yield and Fruit Quality
Agronomy 2020, 10(6), 897; https://doi.org/10.3390/agronomy10060897 - 24 Jun 2020
Abstract
Orchard-side optimization of fruit quality is experiencing renewed research focus in the fresh fruit industry as new technologies and quality metrics have emerged to enhance consumer acceptance and satisfaction. Fruit dry matter, one such quality index gaining traction among numerous fresh fruit commodities, [...] Read more.
Orchard-side optimization of fruit quality is experiencing renewed research focus in the fresh fruit industry as new technologies and quality metrics have emerged to enhance consumer acceptance and satisfaction. Fruit dry matter, one such quality index gaining traction among numerous fresh fruit commodities, was targeted for improvement in d’Anjou pear with the application of seasonal pruning cycles (fall, fall and summer, winter, and winter and summer) across two growing seasons in 2016 and 2017 in a mid-aged, traditionally managed commercial orchard in the Columbia basin, Washington, USA. Dry matter was assessed non-destructively on pears using near-infrared spectroscopy at harvest and fruit categorized in to low (<13%), moderate (13–16%), and high (>16%) dry matter quality categories, revealing that fall pruning positively impacted average predicted fruit dry matter in comparison to winter pruning (15.1 vs. 14.2% in 2016 and 13.7 vs. 13.1% predicted dry matter in 2017 for winter vs. fall pruning, respectively), as well in the abundance of high dry matter fruits. The addition of summer pruning to either fall or winter pruning increased fruit size by up to 13% of proportion of fruits 80 mm or greater in diameter. Further, a tendency for summer pruning to decrease yield (up to nearly 30 kg/tree lower yields), average fruit dry matter (up to 0.5% lower average predicted dry matter), and abundance of high dry matter fruits (up to 11% fewer high predicted dry matter fruits) was observed. Fruit quality classes assembled on predicted dry matter verified the utility of this emerging parameter as a fruit quality metric for pears as demonstrated by more desirable post-harvest eating characteristics such as higher soluble solids content corresponding to greater at-harvest predicted dry matter categories. Targeted seasonal pruning in association with precise at-harvest dry matter fruit sorting may preserve the profitability of pear cultivation through their impact on fruit quality and associated consumer experiences. Full article
(This article belongs to the Special Issue In-Field Estimation of Fruit Quality and Quantity)
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Open AccessArticle
Deep Learning for Mango (Mangifera indica) Panicle Stage Classification
Agronomy 2020, 10(1), 143; https://doi.org/10.3390/agronomy10010143 - 18 Jan 2020
Cited by 1
Abstract
Automated assessment of the number of panicles by developmental stage can provide information on the time spread of flowering and thus inform farm management. A pixel-based segmentation method for the estimation of flowering level from tree images was confounded by the developmental stage. [...] Read more.
Automated assessment of the number of panicles by developmental stage can provide information on the time spread of flowering and thus inform farm management. A pixel-based segmentation method for the estimation of flowering level from tree images was confounded by the developmental stage. Therefore, the use of a single and a two-stage deep learning framework (YOLO and R2CNN) was considered, using either upright or rotated bounding boxes. For a validation image set and for a total panicle count, the models MangoYOLO(-upright), MangoYOLO-rotated, YOLOv3-rotated, R2CNN(-rotated) and R2CNN-upright achieved weighted F1 scores of 76.5, 76.1, 74.9, 74.0 and 82.0, respectively. For a test set of the images of another cultivar and using a different camera, the R2 for machine vision to human count of panicles per tree was 0.86, 0.80, 0.83, 0.81 and 0.76 for the same models, respectively. Thus, there was no consistent benefit from the use of rotated over the use of upright bounding boxes. The YOLOv3-rotated model was superior in terms of total panicle count, and the R2CNN-upright model was more accurate for panicle stage classification. To demonstrate practical application, panicle counts were made weekly for an orchard of 994 trees, with a peak detection routine applied to document multiple flowering events. Full article
(This article belongs to the Special Issue In-Field Estimation of Fruit Quality and Quantity)
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Open AccessArticle
In-Field Estimation of Orange Number and Size by 3D Laser Scanning
Agronomy 2019, 9(12), 885; https://doi.org/10.3390/agronomy9120885 - 13 Dec 2019
Cited by 4
Abstract
The estimation of fruit load of an orchard prior to harvest is useful for planning harvest logistics and trading decisions. The manual fruit counting and the determination of the harvesting capacity of the field results are expensive and time-consuming. The automatic counting of [...] Read more.
The estimation of fruit load of an orchard prior to harvest is useful for planning harvest logistics and trading decisions. The manual fruit counting and the determination of the harvesting capacity of the field results are expensive and time-consuming. The automatic counting of fruits and their geometry characterization with 3D LiDAR models can be an interesting alternative. Field research has been conducted in the province of Cordoba (Southern Spain) on 24 ‘Salustiana’ variety orange trees—Citrus sinensis (L.) Osbeck—(12 were pruned and 12 unpruned). Harvest size and the number of each fruit were registered. Likewise, the unitary weight of the fruits and their diameter were determined (N = 160). The orange trees were also modelled with 3D LiDAR with colour capture for their subsequent segmentation and fruit detection by using a K-means algorithm. In the case of pruned trees, a significant regression was obtained between the real and modelled fruit number (R2 = 0.63, p = 0.01). The opposite case occurred in the unpruned ones (p = 0.18) due to a leaf occlusion problem. The mean diameters proportioned by the algorithm (72.15 ± 22.62 mm) did not present significant differences (p = 0.35) with the ones measured on fruits (72.68 ± 5.728 mm). Even though the use of 3D LiDAR scans is time-consuming, the harvest size estimation obtained in this research is very accurate. Full article
(This article belongs to the Special Issue In-Field Estimation of Fruit Quality and Quantity)
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Open AccessBrief Report
Detection and Characterization of Cherries: A Deep Learning Usability Case Study in Chile
Agronomy 2020, 10(6), 835; https://doi.org/10.3390/agronomy10060835 - 12 Jun 2020
Abstract
Chile is one of the main exporters of sweet cherries in the world and one of the few in the southern hemisphere, being their harvesting between October and January. Hence, Chilean cherries have gained market in the last few years and positioned Chile [...] Read more.
Chile is one of the main exporters of sweet cherries in the world and one of the few in the southern hemisphere, being their harvesting between October and January. Hence, Chilean cherries have gained market in the last few years and positioned Chile in a strategic situation which motivates to undergo through a deep innovation process in the field. Currently, cherry crop estimates have an error of approximately 45%, which propagates to all stages of the production process. In order to mitigate such error, we develop, test and evaluate a deep neural-based approach, using a portable artificial vision system to enhance the cherries harvesting estimates. Our system was tested in a cherry grove, under real field conditions. It was able to detect cherries with up to 85% of accuracy and to estimate production with up to 25% of error. In addition, it was able to classify cherries into four sizes, for a better characterization of the production for exportation. Full article
(This article belongs to the Special Issue In-Field Estimation of Fruit Quality and Quantity)
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