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Agronomy
Special Issues
Applications of Machine Learning and Remote Sensing in Crop and...
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Applications of Machine Learning and Remote Sensing in Crop and Vegetation Monitoring

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 1873

Special Issue Editors

Dr. Yangyang Liu

E-Mail Website
Guest Editor
College of Grassland Agriculture, Northwest A&F University, Yangling 712100, China
Interests: vegetation dynamic remote sensing monitoring; assessment of vegetation ecological service function; ecological hydrology and carbon water cycle
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Zhang

E-Mail Website
Guest Editor
College of Grassland Agriculture, Northwest A&F University, Yangling 712100, China
Interests: grassland ecosystem restoration effect and mechanism; ecosystem carbon nitrogen water cycle and its coupling process; biodiversity and ecosystem service function
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vegetation is a collective term for various plant types that grow on the surface of the Earth and play an important role in the Earth's system. Vegetation is an important regenerative resource within the Earth's surface. Vegetation is the most active and valuable influencing and indicating factor in global change. It simultaneously affects the energy balance of the Earth’s atmosphere system and plays an important role in climate, hydrological, and biochemical cycles.

The combination of remote sensing technology and machine learning technology has brought new solutions to vegetation monitoring. The data obtained through remote sensing technology are automatically analyzed and processed through machine learning algorithms to achieve the efficient and high-precision monitoring of vegetation coverage, biomass, and pests and diseases. This is of great significance for environmental protection and ecological construction. This Special Issue will offer a comprehensive review of the research on the simulation and monitoring of vegetation biomass, vegetation coverage, vegetation phenology, vegetation diseases and pests, and vegetation ecological hydrology using machine learning or remote sensing technology. We kindly invite authors to submit review articles, original research articles, or short communications on topics related to the spatiotemporal change monitoring and driving mechanisms of grasslands, forests, and crops in the context of the application of machine learning or remote sensing technology. As Guest Editors, we look forward to reviewing your relevant contributions to this Special Issue. The specific topics of this Special Issue will include (but are not limited to) the following:

  • Vegetation remote sensing monitoring;
  • Vegetation biomass simulation;
  • Crop disease monitoring;
  • Crop yield prediction;
  • Analysis of vegetation driving mechanism;
  • Vegetation ecological hydrology.

Dr. Yangyang Liu
Dr. Wei Zhang
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

  • machine learning
  • application of remote sensing
  • crop disease
  • vegetation monitoring
  • ecological hydrology

Published Papers (2 papers)

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Research

21 pages, 5297 KiB  
Article
Integrating CEDGAN and FCNN for Enhanced Evaluation and Prediction of Plant Growth Environments in Urban Green Spaces
by Ying Wang, Zhansheng Mao, Hexian Jin, Abbas Shafi, Zhenyu Wang and Dan Liu
Agronomy 2024, 14(5), 938; https://doi.org/10.3390/agronomy14050938 - 30 Apr 2024
Viewed by 409
Abstract
Conducting precise evaluations and predictions of the environmental conditions for plant growth in green spaces is crucial for ensuring their health and sustainability. Yet, assessing the health of urban greenery and the plant growth environment represents a significant and complex challenge within the [...] Read more.
Conducting precise evaluations and predictions of the environmental conditions for plant growth in green spaces is crucial for ensuring their health and sustainability. Yet, assessing the health of urban greenery and the plant growth environment represents a significant and complex challenge within the fields of urban planning and environmental management. This complexity arises from two main challenges: the limitations in acquiring high-density, high-precision data, and the difficulties traditional methods face in capturing and modeling the complex nonlinear relationships between environmental factors and plant growth. In light of the superior spatial interpolation capabilities of CEDGAN (conditional encoder–decoder generative adversarial neural network), notwithstanding its comparative lack of robustness across different subjects, and the excellent ability of FCNN (fully connected neural network) to fit multiple nonlinear equation models, we have developed two models based on these network structures. One model performs high-precision spatial attribute interpolation for urban green spaces, and the other predicts and evaluates the environmental conditions for plant growth within these areas. Our research has demonstrated that, following training with various samples, the CEDGAN network exhibits satisfactory performance in interpolating soil pH values, with an average pixel error below 0.03. This accuracy in predicting both spatial distribution and feature aspects improves with the increase in sample size and the number of controlled sampling points, offering an advanced method for high-precision spatial attribute interpolation in the planning and routine management of urban green spaces. Similarly, FCNN has shown commendable performance in predicting and evaluating plant growth environments, with prediction errors generally less than 0.1. Comparing different network structures, models with fewer hidden layers and nodes yielded superior training outcomes. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Remote Sensing in Crop and Vegetation Monitoring)
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18 pages, 4086 KiB  
Article
Estimation of Plant Height and Biomass of Rice Using Unmanned Aerial Vehicle
by Enze Song, Guangcheng Shao, Xueying Zhu, Wei Zhang, Yan Dai and Jia Lu
Agronomy 2024, 14(1), 145; https://doi.org/10.3390/agronomy14010145 - 8 Jan 2024
Cited by 1 | Viewed by 1008
Abstract
Plant height and biomass are important indicators of rice yield. Here we combined measured plant physiological traits with a crop growth model driven by unmanned aerial vehicle spectral data to quantify the changes in rice plant height and biomass under different irrigation and [...] Read more.
Plant height and biomass are important indicators of rice yield. Here we combined measured plant physiological traits with a crop growth model driven by unmanned aerial vehicle spectral data to quantify the changes in rice plant height and biomass under different irrigation and fertilizer treatments. The study included two treatments: I—water availability factor (i.e., three drought objects, optimal, and excess water); and II—two levels of deep percolation and five nitrogen fertilization doses. The introduced model is extreme learning machine (ELM), back propagation neural network (BPNN), and particle swarm optimization-ELM (PSO-ELM), respectively. The results showed that: (1) Proper water level regulation (3~5 cm) significantly increased the accumulation of spike biomass, which was about 6% higher compared to that under flooded conditions. (2) For plant height inversion, the ELM model was optimal with a mean coefficient of determination of 0.78, a mean root mean square error of 0.26 cm, and a mean performance deviation rate of 2.08. For biomass inversion, the PSO-ELM model was optimal with a mean coefficient of determination of 0.88, a mean root mean square error of 3.8 g, and a mean performance deviation rate of 3.29. This study provided the possible opportunity for large-scale estimations of rice yield under environmental disturbances. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Remote Sensing in Crop and Vegetation Monitoring)
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