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Agronomy
Special Issues
Imaging Technology for Detecting Crops and Agricultural Products—3rd Edition
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Imaging Technology for Detecting Crops and Agricultural Products—3rd Edition

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3071

Special Issue Editors

Dr. Ahmed Kayad

E-Mail Website
Guest Editor
Intermountain Research and Extension Center, University of California, Tulelake, CA 96134, USA
Interests: precision agriculture; remote sensing; digital agriculture; yield monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Ahmed Rady

E-Mail Website
Guest Editor
Food, Water, Waste Research Group (FWW), Faculty of Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, UK
Interests: food safety; food quality; non-destructive sensing for food quality and safety; postharvest engineering; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Imaging applications for use in agriculture are rapidly improving at different scales and have the potential to be key elements of sustainable agricultural intensification systems. In particular, satellite and drone imagery provides solutions for monitoring field crops and their within-field variability regarding crop health status, weed detection, and yield monitoring. Low-altitude imagery and machine vision applications of agricultural products are having a clear impact on sorting and harvesting automation. Moreover, the current availability of multispectral and hyperspectral sensors and images, combined with several data processing and machine-learning techniques, can facilitate unprecedented ideas and applications in agriculture. Imaging applications are usually coupled with machine-learning algorithms as a means of developing classification and regression models. Deep learning is a relatively new machine-learning technique that has become increasingly important in different fields in the agri-food chain, especially with significant advancements in imaging acquisition hardware, as well as the computational power available from personal computers with high-capability GPUs and high-performance cloud-based computational servers. There is no doubt that imaging applications in agriculture will continue to lead to several promising solutions in the current digital agriculture revolution. More research efforts and application ideas are still needed to improve the quality of agricultural products and to support farmers’ decisions in light of the different field and crop conditions. This Special Issue aims to exchange knowledge, ideas, analytical techniques, applications, and experiments that use imagery solutions in the field of agricultural applications.

Dr. Ahmed Kayad
Dr. Ahmed Rady
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

  • digital agriculture
  • remote sensing
  • weed detection
  • drone
  • RGB imaging
  • thermal imagery
  • object detection
  • hyperspectral imaging
  • machine learning
  • deep learning
  • Artificial intelligence
  • Industry 4.0
  • Agriculture 4.0
  • IoT

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Related Special Issue

Published Papers (4 papers)

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Research

21 pages, 5148 KiB  
Article
Model Optimization and Application of Straw Mulch Quantity Using Remote Sensing
by Yuanyuan Liu, Yu Sun, Yueyong Wang, Jun Wang, Xuebing Gao, Libin Wang and Mengqi Liu
Agronomy 2024, 14(10), 2352; https://doi.org/10.3390/agronomy14102352 - 12 Oct 2024
Viewed by 412
Abstract
Straw mulch quantity is an important indicator in the detection of straw returned to the field in conservation tillage, but there is a lack of large-scale automated measurement methods. In this study, we estimated global straw mulch quantity and completed the detection of [...] Read more.
Straw mulch quantity is an important indicator in the detection of straw returned to the field in conservation tillage, but there is a lack of large-scale automated measurement methods. In this study, we estimated global straw mulch quantity and completed the detection of straw returned to the field. We used an unmanned aerial vehicle (UAV) carrying a multispectral camera to acquire remote sensing images of straw in the field. First, the spectral index was selected using the Elastic-net (ENET) algorithm. Then, we used the Genetic Algorithm Hybrid Particle Swarm Optimization (GA-HPSO) algorithm, which embeds crossover and mutation operators from the Genetic Algorithm (GA) into the improved Particle Swarm Optimization (PSO) algorithm to solve the problem of machine learning model prediction performance being greatly affected by parameters. Finally, we used the Monte Carlo method to achieve a global estimation of straw mulch quantity and complete the rapid detection of field plots. The results indicate that the inversion model optimized using the GA-HPSO algorithm performed the best, with the coefficient of determination (R2) reaching 0.75 and the root mean square error (RMSE) only being 0.044. At the same time, the Monte Carlo estimation method achieved an average accuracy of 88.69% for the estimation of global straw mulch quantity, which was effective and applicable in the detection of global mulch quantity. This study provides a scientific reference for the detection of straw mulch quantity in conservation tillage and also provides a reliable model inversion estimation method for the estimation of straw mulch quantity in other crops. Full article
(This article belongs to the Special Issue Imaging Technology for Detecting Crops and Agricultural Products—3rd Edition)
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15 pages, 4873 KiB  
Article
Nondestructively Determining Soluble Solids Content of Blueberries Using Reflection Hyperspectral Imaging Technique
by Guangjun Qiu, Biao Chen, Huazhong Lu, Xuejun Yue, Xiangwu Deng, Haishan Ouyang, Bin Li and Xinyu Wei
Agronomy 2024, 14(10), 2296; https://doi.org/10.3390/agronomy14102296 - 6 Oct 2024
Viewed by 626
Abstract
Effectively detecting the quality of blueberries is crucial for ensuring that high-quality products are supplied to the fresh market. This study developed a nondestructive method for determining the soluble solids content (SSC) of blueberry fruit by using a near-infrared hyperspectral imaging technique. The [...] Read more.
Effectively detecting the quality of blueberries is crucial for ensuring that high-quality products are supplied to the fresh market. This study developed a nondestructive method for determining the soluble solids content (SSC) of blueberry fruit by using a near-infrared hyperspectral imaging technique. The reflection hyperspectral images in the 900–1700 nm waveband range were collected from 480 fresh blueberry samples. An image analysis pipeline was developed to extract the spectrums of blueberries from the hyperspectral images. A regression model for quantifying SSC values was successfully established based on the full range of wavebands, achieving the highest RP2 of 0.8655 and the lowest RMSEP value of 0.4431 °Brix. Furthermore, three variable selection methods, namely the Successive Projections Algorithm (SPA), interval PLS (iPLS), and Genetic Algorithm (GA), were utilized to identify the feature wavebands for modeling. The models calibrated from feature wavebands generated an RMSEP of 0.4643 °Brix, 0.4791 °Brix, and 0.4764 °Brix, as well as the RP2 of 0.8507, 0.8397, and 0.8420 for SPA, iPLS, and GA, respectively. Furthermore, a pseudo-color distribution diagram of the SSC values within blueberries was successfully generated based on established models. This study demonstrated a novel approach for blueberry quality detection and inspection by jointly using hyperspectral imaging and machine learning methodologies. It can serve as a valuable reference for the development of grading equipment systems and portable testing devices for fruit quality assurance. Full article
(This article belongs to the Special Issue Imaging Technology for Detecting Crops and Agricultural Products—3rd Edition)
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25 pages, 8675 KiB  
Article
Estimation of Soil Moisture during Different Growth Stages of Summer Maize under Various Water Conditions Using UAV Multispectral Data and Machine Learning
by Ziqiang Chen, Hong Chen, Qin Dai, Yakun Wang and Xiaotao Hu
Agronomy 2024, 14(9), 2008; https://doi.org/10.3390/agronomy14092008 - 3 Sep 2024
Viewed by 651
Abstract
Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby [...] Read more.
Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model’s robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features. Full article
(This article belongs to the Special Issue Imaging Technology for Detecting Crops and Agricultural Products—3rd Edition)
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26 pages, 5402 KiB  
Article
Potential of Thermal and RGB Imaging Combined with Artificial Neural Networks for Assessing Salt Tolerance of Wheat Genotypes Grown in Real-Field Conditions
by Salah El-Hendawy, Muhammad Usman Tahir, Nasser Al-Suhaibani, Salah Elsayed, Osama Elsherbiny and Hany Elsharawy
Agronomy 2024, 14(7), 1390; https://doi.org/10.3390/agronomy14071390 - 27 Jun 2024
Cited by 1 | Viewed by 928
Abstract
Developing new bread wheat varieties that can be successfully grown in saline conditions has become a pressing task for plant breeders. High-throughput phenotyping tools are crucial for this task. Proximal remote sensing is gaining popularity in breeding programs as a quick, cost-effective, and [...] Read more.
Developing new bread wheat varieties that can be successfully grown in saline conditions has become a pressing task for plant breeders. High-throughput phenotyping tools are crucial for this task. Proximal remote sensing is gaining popularity in breeding programs as a quick, cost-effective, and non-invasive tool to assess canopy structure and physiological traits in large genetic pools. Limited research has been conducted on the effectiveness of combining RGB and thermal imaging to assess the salt tolerance of different wheat genotypes. This study aimed to evaluate the effectiveness of combining several indices derived from thermal infrared and RGB images with artificial neural networks (ANNs) for assessing relative water content (RWC), chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophyll (Chlt), and plant dry weight (PDW) of 18 recombinant inbred lines (RILs) and their 3 parents irrigated with saline water (150 mM NaCl). The results showed significant differences in various traits and indices among the tested genotypes. The normalized relative canopy temperature (NRCT) index exhibited strong correlations with RWC, Chla, Chlb, Chlt, and PDW, with R2 values ranging from 0.50 to 0.73, 0.53 to 0.76, 0.68 to 0.84, 0.68 to 0.84, and 0.52 to 0.76, respectively. Additionally, there was a strong relationship between several RGB indices and measured traits, with the highest R2 values reaching up to 0.70. The visible atmospherically resistant index (VARI), a popular index derived from RGB imaging, showed significant correlations with NRCT, RWC, Chla, Chlb, Chlt, and PDW, with R2 values ranging from 0.49 to 0.62 across two seasons. The different ANNs models demonstrated high predictive accuracy for NRCT and other measured traits, with R2 values ranging from 0.62 to 0.90 in the training dataset and from 0.46 to 0.68 in the cross-validation dataset. Thus, our study shows that integrating high-throughput digital image tools with ANN models can efficiently and non-invasively assess the salt tolerance of a large number of wheat genotypes in breeding programs. Full article
(This article belongs to the Special Issue Imaging Technology for Detecting Crops and Agricultural Products—3rd Edition)
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