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Agronomy
Special Issues
AI, Sensors and Robotics for Smart Agriculture—2nd Edition
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AI, Sensors and Robotics for Smart Agriculture—2nd Edition

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

Deadline for manuscript submissions: 15 August 2024 | Viewed by 3145

Special Issue Editors

Dr. Baohua Zhang

E-Mail Website
Guest Editor
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Interests: quality and safety assessment of agricultural products; harvesting robots; robot vision; robotic grasping; spectral analysis and modeling; robotic systems and their applications in agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Yongliang Qiao

E-Mail Website
Guest Editor
Australian Institute for Machine Learning (AIML), The University of Adelaide, Adelaide 5005, Australia
Interests: Artificial Intelligence; agricultural robots; smart farming; intelligent perception
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food security is an enormous issue for human society, and the traditional labor-based agricultural production model has been unable to meet the increasing needs. With the continuous progress of artificial intelligence (AI), sensors and robotics, smart agriculture is gradually being applied to agricultural production across the world. The purpose of smart agriculture is to enhance the efficiency of agricultural production, improve production and management methods, implement green production and retain the ecological environment. 

Smart agriculture is the deep combination of IoT technology and traditional agriculture. The IoT will elevate the future of agriculture to a new level, with the utilization of smart agriculture becoming increasingly common among farmers. By employing the Internet of Things, sensor technology and agricultural robots, smart agriculture could achieve the precise control and scientific management of the production and operation process, realize the intelligent control of agricultural cultivation, and promote the transformation of agricultural development to intensive and large-scale production. The aim of this Special Issue is to share recent studies and developments in the application of AI, sensors and robots in smart agriculture.

Dr. Baohua Zhang
Dr. Yongliang Qiao
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

  • panchromatic, multispectral, and hyperspectral approaches
  • field phenotyping and yield estimation
  • disease and stress detection
  • computer vision
  • robot sensing systems
  • Artificial Intelligence and machine learning
  • sensor fusion in agri-robotics
  • variable-rate applications
  • farm management information systems
  • remote sensing
  • ICT applications
  • agri-robotics navigation and awareness

Published Papers (4 papers)

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Research

14 pages, 3966 KiB  
Article
Time Series Field Estimation of Rice Canopy Height Using an Unmanned Aerial Vehicle-Based RGB/Multispectral Platform
by Ziqiu Li, Xiangqian Feng, Juan Li, Danying Wang, Weiyuan Hong, Jinhua Qin, Aidong Wang, Hengyu Ma, Qin Yao and Song Chen
Agronomy 2024, 14(5), 883; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy14050883 - 23 Apr 2024
Viewed by 380
Abstract
Crop plant height is a critical parameter for assessing crop physiological properties, such as above-ground biomass and grain yield and crop health. Current dominant plant height estimation methods are based on digital surface model (DSM) and vegetation indexes (VIs). However, DSM-based methods usually [...] Read more.
Crop plant height is a critical parameter for assessing crop physiological properties, such as above-ground biomass and grain yield and crop health. Current dominant plant height estimation methods are based on digital surface model (DSM) and vegetation indexes (VIs). However, DSM-based methods usually estimate plant height by growth stages, which would result in some discontinuity between growth stages due to different fitting curves. Additionally, there has been limited research on the application of VI-based plant height estimation for multiple crop species. Thus, this study investigated the validity and challenges associated with these methods for estimating canopy heights of multi-variety rice throughout the entire growing season. A total of 474 rice varieties were cultivated in a single season, and RGB images including red, green, and blue bands, DSMs, multispectral images including near infrared and red edge bands, and manually measured plant heights were collected in 2022. DSMs and 26 commonly used VIs were employed to estimate rice canopy heights during the growing season. The plant height estimation using DSMs was performed using different quantiles (50th, 75th, and 95th), while two-stage linear regression (TLR) models based on each VI were developed. The DSM-based method at the 95th quantile showed high accuracy, with an R2 value of 0.94 and an RMSE value of 0.06 m. However, the plant height estimation at the early growth stage showed lower effectiveness, with an R2 < 0. For the VIs, height estimation with MTCI yielded the best results, with an R2 of 0.704. The first stage of the TLR model (maximum R2 = 0.664) was significantly better than the second stage (maximum R2 = 0.133), which indicated that the VIs were more suitable for estimating canopy height at the early growth stage. By grouping the 474 varieties into 15 clusters, the R2 values of the VI-based TLR models exhibited inconsistencies across clusters (maximum R2 = 0.984; minimum R2 = 0.042), which meant that the VIs were suitable for estimating canopy height in the cultivation of similar or specific rice varieties. However, the DSM-based method showed little difference in performance among the varieties, which meant that the DSM-based method was suitable for multi-variety rice breeding. But for specific clusters, the VI-based methods were better than the DSM-based methods for plant height estimation. In conclusion, the DSM-based method at the 95th quantile was suitable for plant height estimation in the multi-variety rice breeding process, and we recommend using DSMs for plant height estimation after 26 DAT. Furthermore, the MTCI-based TLR model was suitable for plant height estimation in monoculture planting or as a correction for DSM-based plant height estimation in the pre-growth period of rice. Full article
(This article belongs to the Special Issue AI, Sensors and Robotics for Smart Agriculture—2nd Edition)
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18 pages, 18316 KiB  
Article
Assisted Tea Leaf Picking: The Design and Simulation of a 6-DOF Stewart Parallel Lifting Platform
by Zejun Wang, Chunhua Yang, Raoqiong Che, Hongxu Li, Yaping Chen, Lijiao Chen, Wenxia Yuan, Fang Yang, Juan Tian and Baijuan Wang
Agronomy 2024, 14(4), 844; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy14040844 - 18 Apr 2024
Viewed by 311
Abstract
The 6-DOF Stewart parallel elevation platform serves as the platform for mounting the tea-picking robotic arm, significantly impacting the operational scope, velocity, and harvesting precision of the robotic arm. Utilizing the Stewart setup, a parallel elevation platform with automated lifting and leveling capabilities [...] Read more.
The 6-DOF Stewart parallel elevation platform serves as the platform for mounting the tea-picking robotic arm, significantly impacting the operational scope, velocity, and harvesting precision of the robotic arm. Utilizing the Stewart setup, a parallel elevation platform with automated lifting and leveling capabilities was devised, ensuring precise halts at designated elevations for seamless harvesting operations. The effectiveness of the platform parameter configuration and the reasonableness of the posture changes were verified. Firstly, the planting mode and growth characteristics of Yunnan large-leaf tea trees were analyzed to determine the preset path, posture changes, and mechanism stroke of the Stewart parallel lifting platform, thereby determining the basic design specifications of the platform. Secondly, a 3D model was established using SolidWorks, a robust adaptive PD control model was built using MATLAB for simulation, and dynamic calculations were carried out through data interaction in Simulink and ADAMS. Finally, the rationality of the lifting platform design requirements was determined based on simulation data, a 6-DOF Stewart parallel lifting platform was manufactured, and a motion control system was built for experimental verification according to the design specifications and simulation data. The results showed that the maximum deviation angle around the X, Y, and Z axes was 10°, the maximum lifting distance was 15 cm, the maximum load capacity was 60 kg, the platform response error was within ±0.1 mm, and the stable motion characteristics reached below the millimeter level, which can meet the requirements of automated operation of the auxiliary picking robotic arm. Full article
(This article belongs to the Special Issue AI, Sensors and Robotics for Smart Agriculture—2nd Edition)
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25 pages, 5678 KiB  
Article
Are Supervised Learning Methods Suitable for Estimating Crop Water Consumption under Optimal and Deficit Irrigation?
by Sevim Seda Yamaç, Bedri Kurtuluş, Azhar M. Memon, Gadir Alomair and Mladen Todorovic
Agronomy 2024, 14(3), 532; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy14030532 - 04 Mar 2024
Viewed by 737
Abstract
This study examined the performance of random forest (RF), support vector machine (SVM) and adaptive boosting (AB) machine learning models used to estimate daily potato crop evapotranspiration adjusted (ETc-adj) under full irrigation (I100), 50% of full irrigation supply (I [...] Read more.
This study examined the performance of random forest (RF), support vector machine (SVM) and adaptive boosting (AB) machine learning models used to estimate daily potato crop evapotranspiration adjusted (ETc-adj) under full irrigation (I100), 50% of full irrigation supply (I50) and rainfed cultivation (I0). Five scenarios of weather, crop and soil data availability were considered: (S1) reference evapotranspiration and precipitation, (S2) S1 and crop coefficient, (S3) S2, the fraction of total available water and root depth, (S4) S2 and total soil available water, and (S5) S3 and total soil available water. The performance of machine learning models was compared with the standard FAO56 calculation procedure. The most accurate ETc-adj estimates were observed with AB4 for I100, RF3 for I50 and AB5 for I0 with coefficients of determination (R2) of 0.992, 0.816 and 0.922, slopes of 1.004, 0.999 and 0.972, modelling efficiencies (EF) of 0.992, 0.815 and 0.917, mean absolute errors (MAE) of 0.125, 0.405 and 0.241 mm day−1, root mean square errors (RMSE) of 0.171, 0.579 and 0.359 mm day−1 and mean squared errors (MSE) of 0.029, 0.335 and 0.129 mm day−1, respectively. The AB model is suggested for ETc-adj prediction under I100 and I0 conditions, while the RF model is recommended under the I50 condition. Full article
(This article belongs to the Special Issue AI, Sensors and Robotics for Smart Agriculture—2nd Edition)
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34 pages, 2083 KiB  
Article
Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications
by Claudia Leslie Arellano Vidal and Joseph Edward Govan
Agronomy 2024, 14(2), 341; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy14020341 - 07 Feb 2024
Cited by 1 | Viewed by 1095
Abstract
Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, [...] Read more.
Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector. Full article
(This article belongs to the Special Issue AI, Sensors and Robotics for Smart Agriculture—2nd Edition)
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