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How Optical Sensors and Deep Learning Enhance the Production Management...
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How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 25 July 2025 | Viewed by 4548

Special Issue Editors

Dr. Jibo Yue

E-Mail Website
Guest Editor
College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Interests: remote sensing; precision agriculture; deep learning; crop model; crop mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Meiyan Shu

E-Mail Website
Guest Editor
College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Interests: UAV; precision agriculture; machine learning; crop model; crop mapping
Dr. Chengquan Zhou

E-Mail Website
Guest Editor
Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 31000, China
Interests: UAV; UGV; deep learning; crop model; crop mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Haikuan Feng

E-Mail Website
Guest Editor
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Interests: UAV; biomass; nutrient management; yield mapping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fenghua Yu

E-Mail Website
Guest Editor
College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Interests: RTM; crop model; UAV; crop mapping

Special Issue Information

Dear Colleagues,

Integrating optical sensors and deep learning (DL) in farming has revolutionized traditional agricultural practices. From early simple linear regression to advanced DL-driven predictive analytics, the journey has been marked by significant technological advancements to optimize crop yields and resource management.

This Special Issue aims to highlight the transformative impact of optical sensors and DL on smart agriculture. We seek to highlight innovative applications, address current challenges, and discuss future directions. We invite contributions that showcase the latest research in optical sensor technology and DL applications in agriculture. Topics of interest include but are not limited to the following:

  • DL algorithms for crop monitoring (e.g., crop growth monitoring, crop yield prediction);
  • DL-based real-time crop monitoring solutions for unmanned ground vehicles and aerial vehicles (e.g., crop phenotyping);
  • DL applications for field management (e.g., disease, pest control).

We are soliciting original research articles, review papers, and case studies that provide insights into the practical implementation and benefits of optical sensors and DL in agriculture. We look forward to receiving your contributions, which will continue to drive the future of smart agriculture.

Dr. Jibo Yue
Dr. Meiyan Shu
Dr. Chengquan Zhou
Dr. Haikuan Feng
Prof. Dr. Fenghua Yu
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. Agriculture 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 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

  • optical sensors
  • deep learning
  • crop health monitoring
  • yield prediction
  • crop phenology
  • crop growth monitoring
  • unmanned aerial vehicles
  • unmanned ground vehicles

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

Jump to: Review

22 pages, 3331 KiB  
Article
Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision
by Wanna Fu, Zhen Chen, Qian Cheng, Yafeng Li, Weiguang Zhai, Fan Ding, Xiaohui Kuang, Deshan Chen and Fuyi Duan
Agriculture 2025, 15(12), 1272; https://doi.org/10.3390/agriculture15121272 - 12 Jun 2025
Viewed by 361
Abstract
Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome [...] Read more.
Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R2 values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R2 values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making. Full article
(This article belongs to the Special Issue How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture)
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27 pages, 12293 KiB  
Article
Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning
by Riqiang Chen, Lipeng Ren, Guijun Yang, Zhida Cheng, Dan Zhao, Chengjian Zhang, Haikuan Feng, Haitang Hu and Hao Yang
Agriculture 2025, 15(10), 1072; https://doi.org/10.3390/agriculture15101072 - 16 May 2025
Viewed by 547
Abstract
Leaf chlorophyll content (LCC) serves as a vital biochemical indicator of photosynthetic activity and nitrogen status, critical for precision agriculture to optimize crop management. While UAV-based hyperspectral sensing offers maize LCC estimation potential, current methods struggle with overlapping spectral bands and suboptimal model [...] Read more.
Leaf chlorophyll content (LCC) serves as a vital biochemical indicator of photosynthetic activity and nitrogen status, critical for precision agriculture to optimize crop management. While UAV-based hyperspectral sensing offers maize LCC estimation potential, current methods struggle with overlapping spectral bands and suboptimal model accuracy. To address these limitations, we proposed an integrated maize LCC estimation framework combining UAV hyperspectral imagery, simulated hyperspectral data, E2D-COS feature selection, deep neural network (DNN), and transfer learning (TL). The E2D-COS algorithm with simulated data was used to identify structure-resistant spectral bands strongly correlated with maize LCC: Big trumpet stage: 418 nm, 453 nm, 506 nm, 587 nm, 640 nm, 688 nm, and 767 nm; Spinning stage: 418 nm, 453 nm, 541 nm, 559 nm, 688 nm, 723 nm, and 767 nm. Combining the E2D-COS feature selection with TL and DNN significantly improves the estimation accuracy: the R2 of the proposed Maize-LCNet model is improved by 0.06–0.11 and the RMSE is reduced by 0.57–1.06 g/cm compared with LCNet-field. Compared to the existing studies, this study not only clarifies the spectral bands that are able to estimate maize chlorophyll, but also presents a high-performance, lightweight (fewer input) approach to achieve the accurate estimation of LCC in maize, which can directly support growth monitoring nutrient management at specific growth stages, thus contributing to smart agricultural practices. Full article
(This article belongs to the Special Issue How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture)
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33 pages, 21874 KiB  
Article
An Improved YOLOv8 Model for Detecting Four Stages of Tomato Ripening and Its Application Deployment in a Greenhouse Environment
by Haoran Sun, Qi Zheng, Weixiang Yao, Junyong Wang, Changliang Liu, Huiduo Yu and Chunling Chen
Agriculture 2025, 15(9), 936; https://doi.org/10.3390/agriculture15090936 - 25 Apr 2025
Cited by 1 | Viewed by 597
Abstract
The ripeness of tomatoes is a critical factor influencing both their quality and yield. Currently, the accurate and efficient detection of tomato ripeness in greenhouse environments, along with the implementation of selective harvesting, has become a topic of significant research interest. In response [...] Read more.
The ripeness of tomatoes is a critical factor influencing both their quality and yield. Currently, the accurate and efficient detection of tomato ripeness in greenhouse environments, along with the implementation of selective harvesting, has become a topic of significant research interest. In response to the current challenges, including the unclear segmentation of tomato ripeness stages, low recognition accuracy, and the limited deployment of mobile applications, this study provided a detailed classification of tomato ripeness stages. Through image processing techniques, the issue of class imbalance was addressed. Based on this, a model named GCSS-YOLO was proposed. Feature extraction was refined by introducing the RepNCSPELAN module, which is a lightweight alternative that reduces model size. A multi-dimensional feature neck network was integrated to enhance feature fusion, and three Semantic Feature Learning modules (SGE) were added before the detection head to minimize environmental interference. Further, Shape_IoU replaced CIoU as the loss function, prioritizing bounding box shape and size for improved detection accuracy. Experiments demonstrated GCSS-YOLO’s superiority, achieving an average mean average precision mAP50 of 85.3% and F1 score of 82.4%, outperforming the SSD, RT-DETR, and YOLO variants and advanced models like YOLO-TGI and SAG-YOLO. For practical deployment, this study deployed a mobile application developed using the NCNN framework on the Android platform. Upon evaluation, the model achieved an RMSE of 0.9045, an MAE of 0.4545, and an R2 value of 0.9426, indicating strong performance. Full article
(This article belongs to the Special Issue How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture)
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24 pages, 2214 KiB  
Article
Passion Fruit Disease Detection Using Sparse Parallel Attention Mechanism and Optical Sensing
by Yajie He, Ningyi Zhang, Xinjin Ge, Siqi Li, Linfeng Yang, Minghao Kong, Yiping Guo and Chunli Lv
Agriculture 2025, 15(7), 733; https://doi.org/10.3390/agriculture15070733 - 28 Mar 2025
Cited by 2 | Viewed by 722
Abstract
A disease detection network based on a sparse parallel attention mechanism is proposed and experimentally validated in the passion fruit (Passiflora edulis [Sims]) disease detection task. Passiflora edulis, as a tropical and subtropical fruit tree, is loved worldwide for its unique [...] Read more.
A disease detection network based on a sparse parallel attention mechanism is proposed and experimentally validated in the passion fruit (Passiflora edulis [Sims]) disease detection task. Passiflora edulis, as a tropical and subtropical fruit tree, is loved worldwide for its unique flavor and rich nutritional value. The experimental results demonstrate that the proposed model performs excellently across various metrics, achieving a precision of 0.93, a recall of 0.88, an accuracy of 0.91, an mAP@50 (average precision at the IoU threshold of 0.50) of 0.90, an mAP@50–95 (average precision at IoU thresholds from 0.50 to 0.95) of 0.60, and an F1-score of 0.90, significantly outperforming traditional object detection models such as Faster R-CNN, SSD, and YOLO. The experiments show that the sparse parallel attention mechanism offers significant advantages in disease detection with multi-scale and complex backgrounds. This study proposes a lightweight deep learning model incorporating a sparse parallel attention mechanism (SPAM) for passion fruit disease detection. Built upon a Convolutional Neural Network (CNN) backbone, the model integrates a dynamically selective attention mechanism to enhance detection performance in cases with complex backgrounds and multi-scale objects. Experimental results demonstrate that the model has superior precision, recall, and mean average precision (mAP) compared with state-of-the-art detection models while maintaining computational efficiency. Full article
(This article belongs to the Special Issue How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture)
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20 pages, 9559 KiB  
Article
Estimation Model of Corn Leaf Area Index Based on Improved CNN
by Chengkai Yang, Jingkai Lei, Zhihao Liu, Shufeng Xiong, Lei Xi, Jian Wang, Hongbo Qiao and Lei Shi
Agriculture 2025, 15(5), 481; https://doi.org/10.3390/agriculture15050481 - 24 Feb 2025
Viewed by 806
Abstract
In response to the issues of high complexity and low efficiency associated with the current reliance on manual sampling and instrumental measurement for obtaining maize leaf area index (LAI), this study constructed a maize image dataset comprising 624 images from three growth stages [...] Read more.
In response to the issues of high complexity and low efficiency associated with the current reliance on manual sampling and instrumental measurement for obtaining maize leaf area index (LAI), this study constructed a maize image dataset comprising 624 images from three growth stages of summer maize in the Henan region, namely the jointing stage, small trumpet stage, and large trumpet stage. Furthermore, a maize LAI estimation model named LAINet, based on an improved convolutional neural network (CNN), was proposed. LAI estimation was carried out at these three key growth stages. In this study, the output structure was improved based on the ResNet architecture to adapt to regression tasks. The Triplet module was introduced to achieve feature fusion and self-attention mechanisms, thereby enhancing the accuracy of maize LAI estimation. The model structure was adjusted to enable the integration of growth-stage information, and the loss function was improved to accelerate the convergence speed of the network model. The model was validated on the self-constructed dataset. The results showed that the incorporation of attention mechanisms, integration of growth-stage information, and improvement of the loss function increased the model’s R2 by 0.04, 0.15, and 0.05, respectively. Among these, the integration of growth-stage information led to the greatest improvement, with the R2 increasing directly from 0.54 to 0.69. The improved model, LAINet, achieved an R2 of 0.81, which indicates that it can effectively estimate the LAI of maize. This model can provide information technology support for the phenotypic monitoring of field crops. Full article
(This article belongs to the Special Issue How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture)
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Review

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25 pages, 1595 KiB  
Review
Research Status and Development Trends of Deep Reinforcement Learning in the Intelligent Transformation of Agricultural Machinery
by Jiamuyang Zhao, Shuxiang Fan, Baohua Zhang, Aichen Wang, Liyuan Zhang and Qingzhen Zhu
Agriculture 2025, 15(11), 1223; https://doi.org/10.3390/agriculture15111223 - 4 Jun 2025
Viewed by 693
Abstract
With the acceleration of agricultural intelligent transformation, deep reinforcement learning (DRL), leveraging its adaptive perception and decision-making capabilities in complex environments, has emerged as a pivotal technology in advancing the intelligent upgrade of agricultural machinery and equipment. For example, in UAV path optimization, [...] Read more.
With the acceleration of agricultural intelligent transformation, deep reinforcement learning (DRL), leveraging its adaptive perception and decision-making capabilities in complex environments, has emerged as a pivotal technology in advancing the intelligent upgrade of agricultural machinery and equipment. For example, in UAV path optimization, DRL can help UAVs plan more efficient flight paths to cover more areas in less time. To enhance the systematicity and credibility of this review, this paper systematically examines the application status, key issues, and development trends of DRL in agricultural scenarios, based on the research literature from mainstream Chinese and English databases spanning from 2018 to 2024. From the perspective of algorithm–hardware synergy, the article provides an in-depth analysis of DRL’s specific applications in agricultural ground platform navigation, path planning for intelligent agricultural end-effectors, and autonomous operations of low-altitude unmanned aerial vehicles. It highlights the technical advantages of DRL by integrating typical experimental outcomes, such as improved path-tracking accuracy and optimized spraying coverage. Meanwhile, this paper identifies three major challenges facing DRL in agricultural contexts: the difficulty of dynamic path planning in unstructured environments, constraints imposed by edge computing resources on algorithmic real-time performance, and risks to policy reliability and safety under human–machine collaboration conditions. Looking forward, the DRL-driven smart transformation of agricultural machinery will focus on three key aspects: (1) The first aspect is developing a hybrid decision-making architecture based on model predictive control (MPC). This aims to enhance the strategic stability and decision-making interpretability of agricultural machinery (like unmanned tractors, harvesters, and drones) in complex and dynamic field environments. This is essential for ensuring the safe and reliable autonomous operation of machinery. (2) The second aspect is designing lightweight models that support edge-cloud collaborative deployment. This can meet the requirements of low-latency responses and low-power operation in edge computing scenarios during field operations, providing computational power for the real-time intelligent decision-making of machinery. (3) The third aspect is integrating meta-learning with self-supervised mechanisms. This helps improve the algorithm’s fast generalization ability across different crop types, climates, and geographical regions, ensuring the smart agricultural machinery system has broad adaptability and robustness and accelerating its application in various agricultural settings. This paper proposes research directions from three key dimensions-“algorithm capability enhancement, deployment architecture optimization, and generalization ability improvement”-offering theoretical references and practical pathways for the continuous evolution of intelligent agricultural equipment. Full article
(This article belongs to the Special Issue How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture)
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