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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 9768

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Special Issue Editors

Dr. Toni Kazic

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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

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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

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Published Papers (10 papers)

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Research

14 pages, 2031 KiB

Open AccessArticle

Spraying Wheat Plants with a Drone Moved at Low Altitudes

by Bogusława Berner, Jerzy Chojnacki, Jiří Dvořák, Aleksandra Pachuta, Jan Najser, Leon Kukiełka, Jan Kielar, Tomáš Najser and Marcel Mikeska

Agronomy 2024, 14(9), 1894; https://doi.org/10.3390/agronomy14091894 - 24 Aug 2024

Viewed by 351

Abstract

On a mounted laboratory stand, comparative tests were carried out on the effectiveness of spraying wheat plants with liquid using a multi-rotor drone. The study was undertaken with and without propeller rotations. The lack of rotations simulated spraying by a ground sprayer. The height of the drone’s displacement above the plants was similar to that of the nozzles above the plants used when spraying with field sprayers, 0.5 m and 1.0 m. The speed of the drone movement was 0.57 and 1.0 m·s⁻¹. The effects of the height and speed of the drone’s movement and the impact of the airflow on the volume and uniformity of the liquid application on the plants were assessed. In addition, changes in the transverse distribution of liquid volume in the droplet stream and the transverse distribution of the air velocity in its stream were evaluated. The liquid was sprayed at a constant pressure of 0.2 MPa. The study’s results show that the low height of the drone displacement not only had a strong effect on increasing the liquid volume applied to the plants but also improved the uniformity of application at plant levels. It was also noticed that, at a height of 0.5 m, there was a significant irregularity in the air stream under the drone. Full article

(This article belongs to the Special Issue Advances in Data, Models, and Their Applications in Agriculture)

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14 pages, 3260 KiB

Open AccessArticle

Discrete Element Modelling and Simulation Parameter Calibration for the Growing Media of Seedling Nursery Blocks

by Rangling Li, Wei Jiang, Wei Wang, Jiayu Fan, Yankun Gao and Hongying Wang

Agronomy 2024, 14(8), 1773; https://doi.org/10.3390/agronomy14081773 - 13 Aug 2024

Viewed by 407

Abstract

Using the discrete element method to simulate the interaction between growing media and machinery is an effective method to design seedling machinery and improve the precision of facility horticultural operations. In order to further improve the accuracy of the study on the interaction between seedling block-forming machines and growing media, the growing media used in the production of seedling nursery blocks was taken as the research object, and the Plackett–Burman screening test and Box–Behnken test were conducted based on the discrete element method using the EEPA model to conduct the calibration of discrete element parameters of the growing media. Optimization was conducted with an actual repose angle as the target value, and the optimal combination is as follows: the interparticle collision-recovery coefficient is 0.5066, the collision-recovery coefficient between particles and the geometric model is 0.714, the interparticle dynamic-friction coefficient is 0.381, and the tangential stiffness factor is 0.375. Finally, the soil uniaxial closed compression test was conducted with optimized calibration parameters. The relative error between the maximum axial load on the punch and the measured value in the simulation process was 4.23%, which verified the accuracy and reliability of parameter calibration of the growing media and provided support for the simulation of growing media and optimization of seedling nursery block-forming machine. Full article

(This article belongs to the Special Issue Advances in Data, Models, and Their Applications in Agriculture)

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19 pages, 2418 KiB

Open AccessArticle

Research on Tobacco Field Semantic Segmentation Method Based on Multispectral Unmanned Aerial Vehicle Data and Improved PP-LiteSeg Model

by Jun Zhang, Zhenping Qiang, Hong Lin, Zhuqun Chen, Kaibo Li and Shuang Zhang

Agronomy 2024, 14(7), 1502; https://doi.org/10.3390/agronomy14071502 - 11 Jul 2024

Viewed by 652

Abstract

In recent years, the estimation of tobacco field areas has become a critical component of precision tobacco cultivation. However, traditional satellite remote sensing methods face challenges such as high costs, low accuracy, and susceptibility to noise, making it difficult to meet the demand for high precision. Additionally, optical remote sensing methods perform poorly in regions with complex terrain. Therefore, Unmanned Aerial Vehicle multispectral remote sensing technology has emerged as a viable solution due to its high resolution and rich spectral information. This study employed a DJI Mavic 3M equipped with high-resolution RGB and multispectral cameras to collect tobacco field data covering five bands: RGB, RED, RED EDGE, NIR, and GREEN in Agang Town, Luoping County, Yunnan Province, China. To ensure the accuracy of the experiment, we used 337, 242, and 215 segmented tobacco field images for model training, targeting both RGB channels and seven-channel data. We developed a tobacco field semantic segmentation method based on PP-LiteSeg and deeply customized the model to adapt to the characteristics of multispectral images. The input layer’s channel number was adjusted to multiple channels to fully utilize the information from the multispectral images. The model structure included an encoder, decoder, and SPPM module, which used a multi-layer convolution structure to achieve feature extraction and segmentation of multispectral images. The results indicated that compared to traditional RGB images, multispectral images offered significant advantages in handling edges and complex terrain for semantic segmentation. Specifically, the predicted area using the seven-channel data was 11.43 m² larger than that obtained with RGB channels. Additionally, the seven-channel model achieved a prediction accuracy of 98.84%. This study provides an efficient and feasible solution for estimating tobacco field areas based on multispectral images, offering robust support for modern agricultural management. Full article

(This article belongs to the Special Issue Advances in Data, Models, and Their Applications in Agriculture)

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17 pages, 1643 KiB

Open AccessArticle

Soybean Canopy Stress Classification Using 3D Point Cloud Data

by Therin J. Young, Shivani Chiranjeevi, Dinakaran Elango, Soumik Sarkar, Asheesh K. Singh, Arti Singh, Baskar Ganapathysubramanian and Talukder Z. Jubery

Agronomy 2024, 14(6), 1181; https://doi.org/10.3390/agronomy14061181 - 30 May 2024

Viewed by 752

Abstract

Automated canopy stress classification for field crops has traditionally relied on single-perspective, two-dimensional (2D) photographs, usually obtained through top-view imaging using unmanned aerial vehicles (UAVs). However, this approach may fail to capture the full extent of plant stress symptoms, which can manifest throughout the canopy. Recent advancements in LiDAR technologies have enabled the acquisition of high-resolution 3D point cloud data for the entire canopy, offering new possibilities for more accurate plant stress identification and rating. This study explores the potential of leveraging 3D point cloud data for improved plant stress assessment. We utilized a dataset of RGB 3D point clouds of 700 soybean plants from a diversity panel exposed to iron deficiency chlorosis (IDC) stress. From this unique set of 700 canopies exhibiting varying levels of IDC, we extracted several representations, including (a) handcrafted IDC symptom-specific features, (b) canopy fingerprints, and (c) latent feature-based features. Subsequently, we trained several classification models to predict plant stress severity using these representations. We exhaustively investigated several stress representations and model combinations for the 3-D data. We also compared the performance of these classification models against similar models that are only trained using the associated top-view 2D RGB image for each plant. Among the feature-model combinations tested, the 3D canopy fingerprint features trained with a support vector machine yielded the best performance, achieving higher classification accuracy than the best-performing model based on 2D data built using convolutional neural networks. Our findings demonstrate the utility of color canopy fingerprinting and underscore the importance of considering 3D data to assess plant stress in agricultural applications. Full article

(This article belongs to the Special Issue Advances in Data, Models, and Their Applications in Agriculture)

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13 pages, 704 KiB

Open AccessArticle

Response Surface Methodology for Development of Nutrient Solution Formula for Hydroponic Lettuce Based on the Micro-Elements Fertilizer Requirements at Different Growth Stages

by Binbin Gong, Xiaowei Ren, Wenyu Hao, Jingrui Li, Shenglin Hou, Kun Yang, Xiaolei Wu and Hongbo Gao

Agronomy 2024, 14(6), 1160; https://doi.org/10.3390/agronomy14061160 - 29 May 2024

Viewed by 642

Abstract

In order to precisely obtain the impact of nutritional elements on lettuce yield and quality, in the present study, a nutrient solution formula suitable for lettuce hydroponic production was development using response surface methodology based on the determination for micro-elements in three growth stages and taking the interaction between elements into account. Then, the formula was optimized and validated, aiming for the goal of improving lettuce yield and quality. The results showed that 200 healthy lettuce leaves contained similar amounts of macro-elements, and there was no significant difference in the unit content of micro-elements among the seedling, rosette, and harvest stages. Quadratic regression models between shoot fresh weight, SPAD value, soluble sugar content, Vc content, and nutrient content were established (R² = 0.91, 0.95, 0.98, and 0.81, respectively). The optimal concentrations of P, K, Ca, and Mg obtained by multi-objective optimization of the quadratic regression models for fresh weight, SPAD value, soluble sugar content, and Vc content were 2.71 mmol·L⁻¹, 6.42 mmol·L⁻¹, 5.58 mmol·L⁻¹, and 7.11 mmol·L⁻¹, respectively. The nutrient solution formula (T1) was found to be the optimal nutrient solution formula for improving lettuce growth and quality. Overall, we developed a specific and targeted nutrient solution formulation for lettuce; this formulation not only meets lettuce’s demand for nutrients, but also improves lettuce yield and quality, providing more choices for lettuce production in a region with high salts and high pH in the irrigation water. Full article

(This article belongs to the Special Issue Advances in Data, Models, and Their Applications in Agriculture)

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22 pages, 2522 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1312

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 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

Open AccessArticle

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 858

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 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

Open AccessArticle

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 1062

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1606

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’ 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

Open AccessArticle

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 1030

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 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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