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Applied Sciences
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Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques
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Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 2841

Special Issue Editors

Dr. Soonho Hwang

E-Mail Website
Guest Editor
Department of Agricultural and Biological Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Interests: water quality; hydrology; nutrient management; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Rabin Bhattarai

E-Mail Website
Guest Editor
Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61810, USA
Interests: nonpoint source pollution; water quality; agricultural systems; erosion and sediment control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This journal’s Special Issue entitled “Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques” seeks the most recent works exploring the strategic role of monitoring, modeling, and machine learning (ML) techniques in modern agriculture. The agricultural sector is undergoing a profound transformation with the integration of advanced technologies aimed at enhancing productivity, sustainability, and resilience. Advanced monitoring systems, including IoT sensors and remote sensing technologies, enable real-time data collection on various environmental and crop-specific parameters. These data are instrumental in developing predictive models that simulate and optimize agricultural processes, leading to more precise resource management. Machine learning techniques further enhance these models by analyzing vast datasets to uncover patterns, forecast outcomes, and support decision-making processes. Therefore, this convergence of technologies offers the potential to revolutionize traditional farming practices, driving efficiencies and reducing environmental impacts.

The scope of this Special Issue includes the latest developments in advanced monitoring systems, such as Internet of Things (IoT) sensors, remote sensing technologies, and other innovative tools that enable real-time data collection on various environmental and agricultural parameters. The journal also gives significant focus to the application of various agricultural and hydrological models to simulate agricultural processes, optimize resource use, and enhance decision-making in sustainable agriculture management plans. The journal also places a strong emphasis on the application of machine learning techniques, which can analyze vast datasets to identify patterns, predict outcomes, and support informed decision-making in agriculture.

Dr. Soonho Hwang
Dr. Rabin Bhattarai
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • irrigation
  • drainage
  • nutrient
  • water quality
  • agricultural modeling
  • hydrological modeling
  • machine learning
  • remote sensing
  • uncertainty assessment
  • sustainable agriculture

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Published Papers (4 papers)

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Research

13 pages, 1710 KiB  
Article
Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
by Mingxiong Ou, Yong Zhang, Zhiyong Yu, Jiayao Zhang, Weidong Jia and Xiang Dong
Appl. Sci. 2025, 15(13), 7411; https://doi.org/10.3390/app15137411 - 1 Jul 2025
Abstract
Precise detection of canopy parameters is vital as it offers essential information for pest management in orchards. Among these parameters, leaf area density stands out as a key indicator of orchard canopies. A detection algorithm for leaf area density was proposed, and a [...] Read more.
Precise detection of canopy parameters is vital as it offers essential information for pest management in orchards. Among these parameters, leaf area density stands out as a key indicator of orchard canopies. A detection algorithm for leaf area density was proposed, and a leaf area density detection system for orchard canopies was designed based on the algorithm. By processing the point cloud data acquired by using LiDAR together with the algorithm, the total leaf area of the fitted leaves was calculated. Through an orthogonal regression experiment conducted on a laboratory-simulated canopy, this research established a mathematical calculation model (R2= 0.96) for determining the leaf area density of an orchard canopy. The leaf area density of an orchard canopy can be calculated using the total leaf area of the fitted leaves and an established mathematical model. To assess the accuracy of the detection system, both laboratory-simulated canopy experiments and real orchard canopy experiments were conducted. The results revealed that the absolute value of the mean relative error in the laboratory-simulated canopy experiments was 11.58%, and the absolute value of the mean relative error in the orchard canopy experiments was 16.75%. The research results have confirmed the feasibility of the LiDAR point cloud data processing algorithm. Furthermore, this algorithm can provide theoretical support for the subsequent development of intelligent plant protection equipment in orchards. Full article
(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)
25 pages, 4959 KiB  
Article
Research on Performance Predictive Model and Parameter Optimization of Pneumatic Drum Seed Metering Device Based on Backpropagation Neural Network
by Yilong Pan, Yaxin Yu, Junwei Zhou, Wenbing Qin, Qiang Wang and Yinghao Wang
Appl. Sci. 2025, 15(7), 3682; https://doi.org/10.3390/app15073682 - 27 Mar 2025
Viewed by 254
Abstract
This innovative method improves the inefficient optimization of the parameters of a pneumatic drum seed metering device. The method applies a backpropagation neural network (BPNN) to establish a predictive model and multi-objective particle swarm optimization (MOPSO) to search for the optimal solution. Six [...] Read more.
This innovative method improves the inefficient optimization of the parameters of a pneumatic drum seed metering device. The method applies a backpropagation neural network (BPNN) to establish a predictive model and multi-objective particle swarm optimization (MOPSO) to search for the optimal solution. Six types of small vegetable seeds were selected to conduct orthogonal experiments of seeding performance. The results were used to build a dataset for building a BPNN predictive model according to the inputs of the physical properties of the seed (thousand-grain weight, kernel density, sphericity, and geometric mean diameter) and the parameters of the device (vacuum pressure, drum rotational speed, and suction hole diameter). From this, the model output the seeding performance indices (the missing and reseeding indexes). The MOPSO algorithm uses the BPNN predictive model as a fitness function to search for the optimal solution for three types of seeds, and the optimized results were verified through bench experiments. The results show that the predicted qualified indices for tomato, pepper, and bok choi seeds are 85.50%, 85.52%, and 84.87%, respectively. All the absolute errors between the predicted and experimental results are less than 3%, indicating that the results are reliable and meet the requirements for efficient parameter optimization of a seed metering device. Full article
(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)
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17 pages, 8502 KiB  
Article
A Lightweight Deep Learning Model for Forecasting the Fishing Ground of Purpleback Flying Squid (Sthenoteuthis oualaniensis) in the Northwest Indian Ocean
by Shengmao Zhang, Junlin Chen, Haibin Han, Fenghua Tang, Xuesen Cui and Yongchuang Shi
Appl. Sci. 2025, 15(3), 1219; https://doi.org/10.3390/app15031219 - 24 Jan 2025
Viewed by 825
Abstract
The purpleback flying squid (Sthenoteuthis oualaniensis) is an economically significant cephalopod species in the Northwest Indian Ocean. Predicting its fishing grounds can provide a crucial foundation for fishery management and production. In this research, we collected data from China’s light-purse seine [...] Read more.
The purpleback flying squid (Sthenoteuthis oualaniensis) is an economically significant cephalopod species in the Northwest Indian Ocean. Predicting its fishing grounds can provide a crucial foundation for fishery management and production. In this research, we collected data from China’s light-purse seine fishery in the Northwest Indian Ocean from 2016 to 2020 to train and validate the AlexNet and VGG11 models. We designed a data partitioning method (DPM) to divide the training set into three scenarios, namely DPM-S1, DPM-S2, and DPM-S3. Firstly, DPM-S1 was employed to select the base model (BM). Subsequently, the optimal BM was lightweighted to obtain the optimal model (OM). The OM, known as the AlexNetMini model, has a model size that is one-third of that of the BM-AlexNet model. Our results also showed the following: (1) the F1-scores for AlexNet and AlexNetMini across the datasets DPM-S1, -S2, and -S3 were 0.6957, 0.7505, and 0.7430 for AlexNet and 0.6992, 0.7495, and 0.7486 for AlexNetMini, suggesting that both models exhibited comparable predictive performance; (2) the optimal dropout values for the AlexNetMini model were 0 and 0.2, and the optimal training set proportion was 0.8; (3) AlexNetMini utilized both DPM-S2 and DPM-S3, yielding comparable outcomes. However, given that the training duration for DPM-S3 was relatively shorter, DPM-S3 was selected as the preferred method for data partitioning. The findings of our study indicated that the lightweight model for the purpleback flying squid fishing ground prediction, specifically AlexNetMini, demonstrated superior performance compared to the original AlexNet model, particularly in terms of efficiency. Our study on the lightweight method for deep learning models provided a reference for enhancing the usability of deep learning in fisheries. Full article
(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)
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16 pages, 3168 KiB  
Article
Impact of Subsurface Drainage System Design on Nitrate Loss and Crop Production
by Soonho Hwang, Shailendra Singh, Rabin Bhattarai, Hanseok Jeong and Richard A. Cooke
Appl. Sci. 2024, 14(22), 10180; https://doi.org/10.3390/app142210180 - 6 Nov 2024
Viewed by 1223
Abstract
Subsurface (or tile) drainage offers a valuable solution for enhancing crop productivity in poorly drained soils. However, this practice is also associated with significant nutrient leaching, which can contribute to water quality problems at the regional scale. This research presents the findings from [...] Read more.
Subsurface (or tile) drainage offers a valuable solution for enhancing crop productivity in poorly drained soils. However, this practice is also associated with significant nutrient leaching, which can contribute to water quality problems at the regional scale. This research presents the findings from a 4-year tile depth and spacing study in central Illinois that included three drain spacings (12.2, 18.3, and 24.4 m) and two drain depths (0.8 and 1.1 m) implemented in six plots under the corn and soybean rotation system (plots CS-1 and CS-3: 12.2 m spacing and 1.1 m depth, plots CS-2 and CS-4: 24.4 m spacing and 1.1 m depth, and plots CS-5 and CS-6 18.3 m spacing and 0.8 m depth). Our observations indicate that drain flow and NO3-N losses were higher in plots with narrower drain spacings, while plots with wider drain spacing showed reduced drain flow and NO3-N losses. Specifically, plots set up with drain spacings of 18.3 m and 24.4 m showed significant reductions in drain flow compared to plots featuring a 12.2 m drain spacing. Likewise, plots characterized by 18.3 m and 24.4 m drain spacings (except CS-4) showed better NO3-N retention and lower leaching losses than those with 12.2 m spacing (CS-1 and CS-3). Crop yield results over a 3-year period indicated that CS-2 (wider spacing plot) showed the highest productivity, with up to 13.6% higher yield compared to other plots. Furthermore, when comparing plots with the same drainage designs, CS-2 and CS-4 showed 5.1% to 2.6% higher corn yield (3-year average) compared to CS-1 and CS-3, and CS-5 and CS-6, respectively. Overall, a wider drainage system showed the capacity to export lower nutrient levels while concurrently enhancing productivity. These findings represent that optimizing tile drainage systems can effectively reduce nitrate losses while increasing crop productivity. Full article
(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)
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