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Agronomy
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Advances in Data, Models, and Their Applications in Agriculture
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Advances in Data, Models, and Their Applications in Agriculture

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 4462

Special Issue Editors

Dr. Toni Kazic

E-Mail Website
Guest Editor
Dept. of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65201, USA
Interests: complex phenotypes; maize lesions; computer vision; field phenomics
Dr. Addie M. Thompson

E-Mail Website
Guest Editor
Dept. of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
Interests: quantitative genetics; field phenomics; maize and sogrhum

Special Issue Information

Dear Colleagues,

We invite you to submit your research to this Special Issue of Agronomy. The aim of this Special Issue is to highlight recent advances and open problems in agriculture, agronomy, and related fields. Areas of interest include, but are not limited to, the following:

  • data—collection methods, datasets, and their validation and annotation, particularly at higher throughput and better resolution;
  • models—computational, mathematical, and statistical models for imaging, morphological and developmental reconstruction, phenotypic prediction, and crop growth and physiology;
  • applications to current and novel crops, especially for underserved crops, growing venues, and crop organs.

The state of the art in all of these areas is imperfect; thus, we strongly encourage the contribution of partial results and the highlighting of pitfalls, failures, and open opportunities.

Inquiries are welcome: please email both editors and include "Agronomy Special Issue" in the subject line.

Dr. Toni Kazic
Dr. Addie M. Thompson
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

  • field phenomics
  • crop modelling
  • phenotypic prediction
  • high throughput phenotyping
  • data collection and validation
  • annotation

Published Papers (5 papers)

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Research

22 pages, 2522 KiB  
Article
Screening of Wheat Genotypes for Water Stress Tolerance Using Soil–Water Relationships and Multivariate Statistical Approaches
by Mohamed H. Sheta, Mostafa M. A. Hasham, Kholoud Z. Ghanem, Hala M. Bayomy, Abdel-Nasser A. El-Sheshtawy, Rasha S. El-Serafy and Eman Naif
Agronomy 2024, 14(5), 1029; https://doi.org/10.3390/agronomy14051029 - 12 May 2024
Viewed by 432
Abstract
Drought stress constricts crop production around the world. Employing high-yielding cultivars with drought tolerance might be the ideal professional approach to coping with its detrimental outcomes. As a result, the current study was performed to investigate the sensitivity and tolerance of nine wheat [...] Read more.
Drought stress constricts crop production around the world. Employing high-yielding cultivars with drought tolerance might be the ideal professional approach to coping with its detrimental outcomes. As a result, the current study was performed to investigate the sensitivity and tolerance of nine wheat genotypes to drought stress. In a randomized block design experiment, nine wheat genotypes were subjected to four water treatments: 100%, 85%, 70%, and 55% of the available water (AW). Four water regimes in two growing seasons were counted as eight environmental zones. The leaf’s water relations and photosynthetic pigment were estimated, as well as growth and yield parameters. Univariate and multivariate statistical approaches, including the new method of multi-trait genotype–ideotype distance (MGIDI), were used for evaluation. The analysis of variance revealed that genotype, environment, and their interactions had a highly significant effect on all traits. The same trend was shown by the additive main effects and multiplicative interaction (AMMI) analysis of variance for grain yield across the environments. The AMMI biplot study indicated that the G8 genotype is the most stable in terms of water stress. The G7 genotype can withstand droughts up to 55% of the available water, while the G8 and G3 genotypes can withstand droughts up to 70% of the available water. Based on all examined traits, this index was used to identify the stable genotypes G7, G8, and G3, which can therefore be suggested for cultivation during drought conditions. Furthermore, we found a positive correlation between the MGIDI, ANOVA, and tolerance index results, indicating that the same desirable genotypes of G7 and G8 were identified by these procedures as being highly tolerant and stable across a range of soil moisture conditions. Based on MGIDI analysis, we can recommend that the G7 genotype exhibits higher grain yield and yield-related traits with the best drought-tolerant indices. Full article
(This article belongs to the Special Issue Advances in Data, Models, and Their Applications in Agriculture)
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16 pages, 13094 KiB  
Article
A Comparative Dataset of Annotated Broccoli Heads Recorded with Depth Cameras from a Moving Vehicle
by Oliver Hardy, Karthik Seemakurthy and Elizabeth I. Sklar
Agronomy 2024, 14(5), 964; https://doi.org/10.3390/agronomy14050964 - 3 May 2024
Viewed by 377
Abstract
An extensive, publicly available dataset is presented—the LAR Broccoli dataset—which contains 20,000 manually annotated images of broccoli heads captured from a moving tractor at an organic farm in the UK. The dataset contains images of the same row of broccoli heads recorded at [...] Read more.
An extensive, publicly available dataset is presented—the LAR Broccoli dataset—which contains 20,000 manually annotated images of broccoli heads captured from a moving tractor at an organic farm in the UK. The dataset contains images of the same row of broccoli heads recorded at 30 frames per second (fps) with three different cameras. Two off-the-shelf, relatively low-cost depth-sensing cameras were used, with the tractor moving at a speed of around 1 km/h, in addition to a webcam, with the tractor moving twice as fast. The utility of the dataset is demonstrated in four ways. First, three different state-of-the-art detector models were trained on the dataset, achieving an overall mean Average Precision (mAP) score of over 95% for the best-performing detector. The results validate the utility of the dataset for the standard task of in-field broccoli head recognition. Second, experiments with transfer learning were conducted, initialised with a smaller pre-trained broccoli detection model, and refined with the LAR Broccoli dataset. Third, we assessed the advantages of transfer learning not only using mAP but also according to time and space requirements for training models, which provides a proxy metric for energy efficiency, a practical consideration for real-world model training. Fourth, the cross-camera generalisation among the three camera systems was compared. The results highlight that testing and training detector models using different camera systems can lead to reduced performance, unless the training set also includes some images captured in the same manner as those in the test set. Full article
(This article belongs to the Special Issue Advances in Data, Models, and Their Applications in Agriculture)
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18 pages, 4292 KiB  
Article
An Explanatory Model of Red Lentil Seed Coat Colour to Manage Degradation in Quality during Storage
by Bhawana Bhattarai, James G. Nuttall, Cassandra K. Walker, Ashley J. Wallace, Glenn J. Fitzgerald and Garry J. O’Leary
Agronomy 2024, 14(2), 373; https://doi.org/10.3390/agronomy14020373 - 15 Feb 2024
Viewed by 730
Abstract
This study presents an explanatory biophysical model developed and validated to simulate seed coat colour traits including CIE L*, a*, and b* changes over time for stored lentil cultivars PBA Hallmark, PBA Hurricane, PBA Bolt, and PBA Jumbo2 under diverse storage [...] Read more.
This study presents an explanatory biophysical model developed and validated to simulate seed coat colour traits including CIE L*, a*, and b* changes over time for stored lentil cultivars PBA Hallmark, PBA Hurricane, PBA Bolt, and PBA Jumbo2 under diverse storage conditions. The model showed robust performance for all cultivars, with R2 values ≥ 0.89 and RMSE values ≤ 0.0019 for all seed coat colour traits. Laboratory validation at 35 °C demonstrated a high agreement (Lin’s Concordance Correlation Coefficient, CCC ≥ 0.82) between simulated and observed values of all colour traits for PBA Jumbo2 and strong agreement (CCC ≥ 0.81) for PBA Hallmark in brightness (CIE L*) and redness (CIE a*), but not in yellowness (CIE b*). At 15 °C, both cultivars exhibited moderate to weak agreement between simulated and observed values of all colour traits (CCC ≤ 0.47), as very little change was recorded in the observed values over the 360 days of storage. Bulk storage system validation for PBA Hallmark showed moderate performance (CCC ≥ 0.46) between simulated and observed values of all colour traits. Modelling to simulate changes in seed coat colour traits of lentils over time will equip growers and traders to make informed managerial decisions when storing lentils for long periods. Full article
(This article belongs to the Special Issue Advances in Data, Models, and Their Applications in Agriculture)
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21 pages, 2516 KiB  
Article
Automated Counting of Tobacco Plants Using Multispectral UAV Data
by Hong Lin, Zhuqun Chen, Zhenping Qiang, Su-Kit Tang, Lin Liu and Giovanni Pau
Agronomy 2023, 13(12), 2861; https://doi.org/10.3390/agronomy13122861 - 21 Nov 2023
Viewed by 1295
Abstract
Plant counting is an important part in precision agriculture (PA). The Unmanned Aerial Vehicle (UAV) becomes popular in agriculture because it can capture data with higher spatiotemporal resolution. When it is equipped with multispectral sensors, more meaningful multispectral data is obtained for plants’ [...] Read more.
Plant counting is an important part in precision agriculture (PA). The Unmanned Aerial Vehicle (UAV) becomes popular in agriculture because it can capture data with higher spatiotemporal resolution. When it is equipped with multispectral sensors, more meaningful multispectral data is obtained for plants’ analysis. After tobacco seedlings are raised, they are transplanted into the field. The counting of tobacco plant stands in the field is important for monitoring the transplant survival rate, growth situation, and yield estimation. In this work, we adopt the object detection (OD) method of deep learning to automatically count the plants with multispectral images. For utilizing the advanced YOLOv8 network, we modified the architecture of the network to adapt to the different band combinations and conducted extensive data pre-processing work. The Red + Green + NIR combination obtains the best detection results, which reveal that using a specific band or band combinations can obtain better results than using the traditional RGB images. For making our method more practical, we designed an algorithm that can handling the image of a whole plot, which is required to be watched. The counting accuracy is as high as 99.53%. The UAV, multispectral data combined with the powerful deep learning methods show promising prospective in PA. Full article
(This article belongs to the Special Issue Advances in Data, Models, and Their Applications in Agriculture)
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14 pages, 9532 KiB  
Article
A Predictive Study on the Content of Epigallocatechin Gallate (EGCG) in Yunnan Large Leaf Tea Trees Based on the Nomogram Model
by Baijuan Wang, Chunhua Yang, Shihao Zhang, Junjie He, Xiujuan Deng, Jun Gao, Lei Li, Yamin Wu, Zongpei Fan, Yuxin Xia, Qicong Guo, Wenxia Yuan and Yuefei Wang
Agronomy 2023, 13(10), 2475; https://doi.org/10.3390/agronomy13102475 - 25 Sep 2023
Viewed by 849
Abstract
To explore the changes in epigallocatechin gallate (EGCG) content in tea under abiotic stress conditions, we collected tea samples, along with corresponding soil and altitude data, and utilized the measured data for single-factor analysis. At the same time, the LASSO regression method, which [...] Read more.
To explore the changes in epigallocatechin gallate (EGCG) content in tea under abiotic stress conditions, we collected tea samples, along with corresponding soil and altitude data, and utilized the measured data for single-factor analysis. At the same time, the LASSO regression method, which is rarely used in agriculture, was employed to screen modeling factors, a prediction model was established, and the Akaike information criterion (AIC) was introduced to compare the goodness of fit. The results show that LASSO screening reduced the AIC value of the model by 13.8%. The average area under the curve of the training set and the validation set was 0.81 and 0.76, respectively, and the calibration curve also showed good consistency. Based on the nomogram model, a visual prediction system was developed, and the content prediction curve was introduced for detailed soil evaluation. The accuracy rate reached 75% after external verification. This study provides a theoretical basis for elucidating the prediction and intervention of Pu’er tea quality under abiotic stress conditions. Full article
(This article belongs to the Special Issue Advances in Data, Models, and Their Applications in Agriculture)
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