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Remote Sensing
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Advanced Pattern Recognition and Image Processing Technology for Agricultural Engineering
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Advanced Pattern Recognition and Image Processing Technology for Agricultural Engineering

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 4525

Special Issue Editors

Dr. Jiehao Li

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Guest Editor
Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: agricultural robotics; image processing; motion control; neural networks; artificial intelligence; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shan Zeng

E-Mail Website
Guest Editor
Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: agricultural mechanization; intelligent agricultural machinery; precision seeding; harvesting technologies
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiwen Luo

E-Mail Website
Guest Editor
The Academician of the Chinese Academy of Engineering, College of Engineering, South China Agricultural University, Guangzhou, China
Interests: agricultural machinery; rice precision direct seeding technology; farmland precision leveling technology; navigation and automatic operation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chenguang Yang

E-Mail Website
Guest Editor
Department of Computer Science, University of Liverpool, Liverpool L693BX, UK
Interests: human robot interaction; intelligent system design
Special Issues, Collections and Topics in MDPI journals
Dr. Jochem Verrelst

E-Mail Website
Guest Editor
Laboratory for Earth Observation, Image Processing Laboratory—Scientific Park, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Valencia, Spain
Interests: imaging spectroscopy; vegetation properties retrieval; FLEX, vegetation fluorescence; optical remote sensing; radiative transfer models; retrieval methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There has been a growing interest in agricultural engineering regarding advanced pattern recognition and image processing technology in recent years. This technology finds application in various agricultural tasks such as fruit-picking robots, pest monitoring, environmental factor tracking for growth, management of agricultural planting, and enhancing seed quality breeding programs. However, the complexity of agricultural environments challenges effective pattern recognition and image processing. External factors often interfere, leading to misclassifications and errors in experimental outcomes. Moreover, despite the rapid advancement of artificial intelligence algorithms, applying pattern recognition and image processing in agriculture encounters several hurdles. These include product overlap, significant occlusion of detection targets, excessive detection of targets, and complications in image processing due to lighting and camera angles. These challenges impede the seamless integration of image-processing technology in complex agricultural settings. Nonetheless, pursuing advanced pattern recognition and image processing in agriculture remains a promising and compelling area of research. This Special Issue aims to present state-of-the-art research achievements and advances by world-class researchers contributing to the agricultural field in pattern recognition, image processing, environment perception, and sensor fusion. Review articles are also encouraged. The potential topics of this organized session include but are not limited to:

  • advanced pattern recognition technology in agricultural engineering;
  • image processing networks in agricultural applications;
  • soil spectral data in agricultural engineering;
  • multispectral image processing in agricultural engineering;
  • satellite remote sensing technology in agriculture;
  • neural networks applications in agriculture engineering;
  • human–machine intelligent algorithm in agriculture;
  • detection and location of agricultural robotics;
  • near-infrared image processing in agricultural engineering;
  • hyperspectral technology in crop monitoring;
  • integrating perception, sensor fusion, and control in agricultural applications;
  • fault detection and diagnosis in agricultural engineering.

Dr. Jiehao Li
Prof. Dr. Shan Zeng
Prof. Dr. Xiwen Luo
Prof. Dr. Chenguang Yang
Dr. Jochem Verrelst
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. Remote Sensing 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 2700 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

  • advanced pattern recognition technology in agricultural engineering
  • image processing networks in agricultural applications
  • soil spectral data in agricultural engineering
  • multispectral image processing in agricultural engineering
  • satellite remote sensing technology in agriculture
  • neural networks applications in agriculture engineering
  • human–machine intelligent algorithm in agriculture
  • detection and location of agricultural robotics
  • near-infrared image processing in agricultural engineering
  • hyperspectral technology in crop monitoring
  • integrating perception, sensor fusion, and control in agricultural applications
  • fault detection and diagnosis in agricultural engineering

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

Published Papers (3 papers)

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Research

18 pages, 4247 KiB  
Article
Temporal Vine Water Status Modeling Through Machine Learning Ensemble Technique and Sentinel-2 Multispectral Images Under Semi-Arid Conditions
by Vincenzo Giannico, Simone Pietro Garofalo, Luca Brillante, Pietro Sciusco, Mario Elia, Giuseppe Lopriore, Salvatore Camposeo, Raffaele Lafortezza, Giovanni Sanesi and Gaetano Alessandro Vivaldi
Remote Sens. 2024, 16(24), 4784; https://doi.org/10.3390/rs16244784 - 22 Dec 2024
Cited by 1 | Viewed by 984
Abstract
New challenges will be experienced by the agriculture sector in the near future, especially due to the effects of climate change. For example, rising temperatures could result in increased evapotranspiration demand, causing difficulties in the management of irrigation practices. Generally, an important predictor [...] Read more.
New challenges will be experienced by the agriculture sector in the near future, especially due to the effects of climate change. For example, rising temperatures could result in increased evapotranspiration demand, causing difficulties in the management of irrigation practices. Generally, an important predictor of plant water status to be taken into account for irrigation monitoring and management is the stem water potential. However, it requires a huge amount of time-consuming fieldwork, particularly when an adequate data amount is necessary to fully investigate the spatial and temporal variability of large areas under monitoring. In this study, the integration of machine learning and satellite remote sensing (Sentinel-2) was investigated to obtain a model able to predict the stem water potential in viticulture using multispectral imagery. Vine water status data were acquired within a Montepulciano vineyard in the south of Italy (Puglia region), under semi-arid conditions; data were acquired over two years during the irrigation seasons. Different machine learning algorithms (lasso, ridge, elastic net, and random forest) were compared using vegetation indices and spectral bands as predictors in two independent analyses. The results show that it is possible to remotely estimate vine water status with random forest from vegetation indices (R2 = 0.72). Integrating machine learning techniques and satellite remote sensing could help farmers and technicians manage and plan irrigation, avoiding or reducing fieldwork. Full article
(This article belongs to the Special Issue Advanced Pattern Recognition and Image Processing Technology for Agricultural Engineering)
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15 pages, 5689 KiB  
Article
Modelling Water Availability in Livestock Ponds by Remote Sensing: Enhancing Management in Iberian Agrosilvopastoral Systems
by Francisco Manuel Castaño-Martín, Álvaro Gómez-Gutiérrez and Manuel Pulido-Fernández
Remote Sens. 2024, 16(17), 3257; https://doi.org/10.3390/rs16173257 - 2 Sep 2024
Viewed by 1080
Abstract
Extensive livestock farming plays a crucial role in the economy of agrosilvopastoral systems of the southwestern Iberian Peninsula (known as dehesas and montados in Spanish and Portuguese, respectively) as well as providing essential ecosystem services. The existence of livestock in these areas heavily [...] Read more.
Extensive livestock farming plays a crucial role in the economy of agrosilvopastoral systems of the southwestern Iberian Peninsula (known as dehesas and montados in Spanish and Portuguese, respectively) as well as providing essential ecosystem services. The existence of livestock in these areas heavily relies on the effective management of natural resources (annual pastures and water stored in ponds built ad hoc). The present work aims to assess the water availability in these ponds by developing equations to estimate the water volume based on the surface area, which can be quantified by means of remote sensing techniques. For this purpose, field surveys were carried out in September 2021, 2022 and 2023 at ponds located in representative farms, using unmanned aerial vehicles (UAVs) equipped with RGB sensors and survey-grade global navigation satellite systems and inertial measurement units (GNSS-IMU). These datasets were used to produce high-resolution 3D models by means of Structure-from-Motion and Multi-View Stereo photogrammetry, facilitating the estimation of the stored water volume within a Geographic Information System (GIS). The Volume–Area–Height relationships were calibrated to allow conversions between these parameters. Regression analyses were performed using the maximum volume and area data to derive mathematical models (power and quadratic functions) that resulted in significant statistical relationships (r2 > 0.90, p < 0.0001). The root mean square error (RMSE) varied from 1.59 to 17.06 m3 and 0.16 to 3.93 m3 for the power and quadratic function, respectively. Both obtained equations (i.e., power and quadratic general functions) were applied to the estimated water storage in similar water bodies using available aerial or satellite imagery for the period from 1984 to 2021. Full article
(This article belongs to the Special Issue Advanced Pattern Recognition and Image Processing Technology for Agricultural Engineering)
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22 pages, 59110 KiB  
Article
Pineapple Detection with YOLOv7-Tiny Network Model Improved via Pruning and a Lightweight Backbone Sub-Network
by Jiehao Li, Yaowen Liu, Chenglin Li, Qunfei Luo and Jiahuan Lu
Remote Sens. 2024, 16(15), 2805; https://doi.org/10.3390/rs16152805 - 31 Jul 2024
Cited by 3 | Viewed by 1650
Abstract
High-complexity network models are challenging to execute on agricultural robots with limited computing capabilities in a large-scale pineapple planting environment in real time. Traditional module replacement often struggles to reduce model complexity while maintaining stable network accuracy effectively. This paper investigates a pineapple [...] Read more.
High-complexity network models are challenging to execute on agricultural robots with limited computing capabilities in a large-scale pineapple planting environment in real time. Traditional module replacement often struggles to reduce model complexity while maintaining stable network accuracy effectively. This paper investigates a pineapple detection framework with a YOLOv7-tiny model improved via pruning and a lightweight backbone sub-network (the RGDP-YOLOv7-tiny model). The ReXNet network is designed to significantly reduce the number of parameters in the YOLOv7-tiny backbone network layer during the group-level pruning process. Meanwhile, to enhance the efficacy of the lightweight network, a GSConv network has been developed and integrated into the neck network, to further diminish the number of parameters. In addition, the detection network incorporates a decoupled head network aimed at separating the tasks of classification and localization, which can enhance the model’s convergence speed. The experimental results indicate that the network before pruning optimization achieved an improvement of 3.0% and 2.2%, in terms of mean average precision and F1 score, respectively. After pruning optimization, the RGDP-YOLOv7-tiny network was compressed to just 2.27 M in parameter count, 4.5 × 109 in computational complexity, and 5.0MB in model size, which were 37.8%, 34.1%, and 40.7% of the original YOLOv7-tiny network, respectively. Concurrently, the mean average precision and F1 score reached 87.9% and 87.4%, respectively, with increases of 0.8% and 1.3%. Ultimately, the model’s generalization performance was validated through heatmap visualization experiments. Overall, the proposed pineapple object detection framework can effectively enhance detection accuracy. In a large-scale fruit cultivation environment, especially under the constraints of hardware limitations and limited computational power in the real-time detection processes of agricultural robots, it facilitates the practical application of artificial intelligence algorithms in agricultural engineering. Full article
(This article belongs to the Special Issue Advanced Pattern Recognition and Image Processing Technology for Agricultural Engineering)
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