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Agronomy
Special Issues
Precision Management to Promote Fruit Yield and Quality in Orchards
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Precision Management to Promote Fruit Yield and Quality in Orchards

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 23302

Special Issue Editors

Dr. Jaume Arnó

E-Mail Website
Guest Editor
Research Group in AgroICT & Precision Agriculture, University of Lleida, Agrotecnio-CERCA Center, 25198 Lleida, Spain
Interests: precision agriculture; geostatistics and spatial data analysis; sampling for fruit yield estimation; machine learning applications in fruit growing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. José A. Martínez-Casasnovas

E-Mail Website
Guest Editor
Research Group in AgroICT & Precision Agriculture, University of Lleida, Agrotecnio-CERCA Center, 25198 Lleida, Spain
Interests: precision agriculture; remote sensing; digital soil mapping; spatial data analysis; site-specific crop management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision Agriculture technologies are being increasingly adopted to promote resource efficiency, productivity, and quality. As these are especially important objectives in orchards, any tools and management methods that enhance fruit yield and quality are of great interest to fruit growers and advisers. This Special Issue will collect the most recent advances in the fruit sector as a result of successfully applying methods to achieve an optimal balance of this double production-quality objective. Proximal and remote sensors for vegetation monitoring, machine learning, and artificial intelligence for data analysis and decision-making, and variable-rate technologies for efficient input application are the most relevant aspects that are included in this Special Issue.

Dr. Jaume Arnó
Prof. Dr. José A. Martínez-Casasnovas
Guest Editors

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 submissions that pass pre-check are 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 2600 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.

Keywords

  • precision horticulture
  • proximal and remote sensing
  • machine learning in fruit growing
  • variable-rate technology
  • artificial intelligence

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Published Papers (3 papers)

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Research

12 pages, 12124 KiB  
Article
Strawberry Water Content Estimation and Ripeness Classification Using Hyperspectral Sensing
by Rahul Raj, Akansel Cosgun and Dana Kulić
Agronomy 2022, 12(2), 425; https://doi.org/10.3390/agronomy12020425 - 8 Feb 2022
Cited by 23 | Viewed by 9126
Abstract
We propose data-driven approaches to water content estimation and ripeness classification of the strawberry fruit. A narrowband hyperspectral spectroradiometer was used to collect reflectance signatures from 43 strawberry fruits at different ripeness levels. Then, the ground truth water content was obtained using the [...] Read more.
We propose data-driven approaches to water content estimation and ripeness classification of the strawberry fruit. A narrowband hyperspectral spectroradiometer was used to collect reflectance signatures from 43 strawberry fruits at different ripeness levels. Then, the ground truth water content was obtained using the oven-dry method. To estimate the water content, 674 and 698 nm bands were selected to create a normalized difference strawberry water content index. The index was used as an input to a logarithmic model for estimating fruit water content. The model for water content estimation gave a correlation coefficient of 0.82 and Root Mean Squared Error (RMSE) of 0.0092 g/g. For ripeness classification, a Support Vector Machine (SVM) model using the full spectrum as input achieved over 98% accuracy. Our analysis further show that, in the absence of the full spectrum data, using our proposed water content index as input, which uses reflectance values from only two frequency bands, achieved 71% ripeness classification accuracy, which might be adequate for certain applications with limited sensing resources. Full article
(This article belongs to the Special Issue Precision Management to Promote Fruit Yield and Quality in Orchards)
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17 pages, 11037 KiB  
Article
Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms
by Marco Sozzi, Silvia Cantalamessa, Alessia Cogato, Ahmed Kayad and Francesco Marinello
Agronomy 2022, 12(2), 319; https://doi.org/10.3390/agronomy12020319 - 26 Jan 2022
Cited by 146 | Viewed by 10023
Abstract
Over the last few years, several Convolutional Neural Networks for object detection have been proposed, characterised by different accuracy and speed. In viticulture, yield estimation and prediction is used for efficient crop management, taking advantage of precision viticulture techniques. Convolutional Neural Networks for [...] Read more.
Over the last few years, several Convolutional Neural Networks for object detection have been proposed, characterised by different accuracy and speed. In viticulture, yield estimation and prediction is used for efficient crop management, taking advantage of precision viticulture techniques. Convolutional Neural Networks for object detection represent an alternative methodology for grape yield estimation, which usually relies on manual harvesting of sample plants. In this paper, six versions of the You Only Look Once (YOLO) object detection algorithm (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv5x, and YOLOv5s) were evaluated for real-time bunch detection and counting in grapes. White grape varieties were chosen for this study, as the identification of white berries on a leaf background is trickier than red berries. YOLO models were trained using a heterogeneous dataset populated by images retrieved from open datasets and acquired on the field in several illumination conditions, background, and growth stages. Results have shown that YOLOv5x and YOLOv4 achieved an F1-score of 0.76 and 0.77, respectively, with a detection speed of 31 and 32 FPS. Differently, YOLO5s and YOLOv4-tiny achieved an F1-score of 0.76 and 0.69, respectively, with a detection speed of 61 and 196 FPS. The final YOLOv5x model for bunch number, obtained considering bunch occlusion, was able to estimate the number of bunches per plant with an average error of 13.3% per vine. The best combination of accuracy and speed was achieved by YOLOv4-tiny, which should be considered for real-time grape yield estimation, while YOLOv3 was affected by a False Positive–False Negative compensation, which decreased the RMSE. Full article
(This article belongs to the Special Issue Precision Management to Promote Fruit Yield and Quality in Orchards)
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15 pages, 4845 KiB  
Article
Delineation of Management Zones in Hedgerow Almond Orchards Based on Vegetation Indices from UAV Images Validated by LiDAR-Derived Canopy Parameters
by José A. Martínez-Casasnovas, Leire Sandonís-Pozo, Alexandre Escolà, Jaume Arnó and Jordi Llorens
Agronomy 2022, 12(1), 102; https://doi.org/10.3390/agronomy12010102 - 31 Dec 2021
Cited by 10 | Viewed by 2963
Abstract
One of the challenges in orchard management, in particular of hedgerow tree plantations, is the delineation of management zones on the bases of high-precision data. Along this line, the present study analyses the applicability of vegetation indices derived from UAV images to estimate [...] Read more.
One of the challenges in orchard management, in particular of hedgerow tree plantations, is the delineation of management zones on the bases of high-precision data. Along this line, the present study analyses the applicability of vegetation indices derived from UAV images to estimate the key structural and geometric canopy parameters of an almond orchard. In addition, the classes created on the basis of the vegetation indices were assessed to delineate potential management zones. The structural and geometric orchard parameters (width, height, cross-sectional area and porosity) were characterized by means of a LiDAR sensor, and the vegetation indices were derived from a UAV-acquired multispectral image. Both datasets summarized every 0.5 m along the almond tree rows and were used to interpolate continuous representations of the variables by means of geostatistical analysis. Linear and canonical correlation analyses were carried out to select the best performing vegetation index to estimate the structural and geometric orchard parameters in each cross-section of the tree rows. The results showed that NDVI averaged in each cross-section and normalized by its projected area achieved the highest correlations and served to define potential management zones. These findings expand the possibilities of using multispectral images in orchard management, particularly in hedgerow plantations. Full article
(This article belongs to the Special Issue Precision Management to Promote Fruit Yield and Quality in Orchards)
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