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Agriculture
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Novel Applications of Optical Sensors and Machine Learning in Agricultural...
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Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring—2nd Edition

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

Deadline for manuscript submissions: 25 June 2024 | Viewed by 3305

Special Issue Editors

Dr. Haikuan Feng

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Guest Editor
Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, 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
Dr. Yanjun Yang

E-Mail Website
Guest Editor
Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
Interests: remote sensing; climate change; machine learning; ecosystem model
Special Issues, Collections and Topics in MDPI journals
Dr. Ning Zhang

E-Mail Website
Guest Editor
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: UAV; smart orchard; pest management; pest risk mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Chengquan Zhou

E-Mail Website
Guest Editor
Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
Interests: image segmentation; UAV; machine learning; pattern recognition; IOT
Special Issues, Collections and Topics in MDPI journals
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; machine learning; crop model; crop mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agricultural production management is facing a new era of intelligence and automation. With developments in sensor technologies, temporal, spectral, and spatial resolution from ground/air/space platforms have been notably improved. Optical sensors play an essential role in agriculture production management. In particular, monitoring plant health, growth condition, and insect infestation have traditionally been approached by performing extensive fieldwork.

The processing and analysis of huge amounts of data from different sensors still face many challenges. Machine learning can derive and process agricultural information from optical sensors onboard ground, air, and space platforms. Advances in optical images and machine learning have attracted widespread attention, but we call for more highly flexible solutions for various agricultural study applications.

We believe that sensors, artificial intelligence, and machine learning are not simply scientific experiments but opportunities to make our agricultural production management more efficient and cost-effective, further contributing to the healthy development of nature–human systems.

This Topic seeks to compile the latest research on optical sensors and machine learning in agricultural monitoring. The following provides a general (but not exhaustive) overview of subjects that might be relevant to this Topic:

  • Machine learning approaches for crop health, growth, and yield monitoring.
  • Combined multisource/multi-sensor data to improve crop parameter mapping.
  • Crop-related growth models, artificial intelligence models, algorithms, and precision management.
  • Farmland environmental monitoring and management.
  • Ground, air, and space platform application in precision agriculture.
  • Development and application of field robotics.
  • High-throughput field information surveys.
  • Phenological monitoring.

Dr. Haikuan Feng
Dr. Yanjun Yang
Dr. Ning Zhang
Dr. Chengquan Zhou
Dr. Jibo Yue
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 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
  • deep learning
  • optical sensor
  • crop mapping
  • precision agriculture

Published Papers (4 papers)

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Research

20 pages, 4620 KiB  
Article
Estimating Corn Growth Parameters by Integrating Optical and Synthetic Aperture Radar Features into the Water Cloud Model
by Yanyan Wang, Zhaocong Wu, Shanjun Luo, Xinyan Liu, Shuaibing Liu and Xinxin Huang
Agriculture 2024, 14(5), 695; https://doi.org/10.3390/agriculture14050695 - 28 Apr 2024
Viewed by 312
Abstract
Crop growth parameters are the basis for evaluation of crop growth status and crop yield. The aim of this study was to develop a more accurate estimation model for corn growth parameters combined with multispectral vegetation indexes (VIopt) and the differential [...] Read more.
Crop growth parameters are the basis for evaluation of crop growth status and crop yield. The aim of this study was to develop a more accurate estimation model for corn growth parameters combined with multispectral vegetation indexes (VIopt) and the differential radar information (DRI) derived from SAR data. Targeting the estimation of corn plant height (H) and the BBCH (Biologische Bundesanstalt, Bundessortenamt and CHemical industry) phenological parameters, this study compared the estimation accuracies of various multispectral vegetation indexes (VIopt) and the corresponding VIDRI (vegetation index corrected by DRI) indexes in inverting the corn growth parameters. (1) When comparing the estimation accuracies of four multispectral vegetation indexes (NDVI, NDVIre1, NDVIre2, and S2REP), NDVI showed the lowest estimation accuracy, with a normalized root mean square error (nRMSE) of 20.84% for the plant height, while S2REP showed the highest estimation accuracy (nRMSE = 16.05%). In addition, NDVIre2 (nRMSE = 16.18%) and S2REP (16.05%) exhibited a higher accuracy than NDVIre1 (nRMSE = 19.27%). Similarly, for BBCH, the nRMSEs of the four indexes were 24.17%, 22.49%, 17.04% and 16.60%, respectively. This confirmed that the multispectral vegetation indexes based on the red-edge bands were more sensitive to the growth parameters, especially for the Sentinel-2 red-edge 2 band. (2) The constructed VIDRI indexes were more beneficial than the VIopt indexes in enhancing the estimation accuracy of corn growth parameters. Specifically, the nRMSEs of the four VIDRI indexes (NDVIDRI, NDVIre1DRI, NDVIre2DRI, and S2REPDRI) decreased to 19.64%, 18.11%, 15.00%, and 14.64% for plant height, and to 23.24%, 21.58%, 15.79%, and 15.91% for BBCH, indicating that even in cases of high vegetation coverage, the introduction of SAR DRI features can further improve the estimation accuracy of growth parameters. Our findings also demonstrated that the NDVIre2DRI and S2REPDRI indexes constructed using red-edge 2 band information of Sentinel-2 and SAR DRI features had more advantages in improving the estimation accuracy of corn growth parameters. Full article
(This article belongs to the Special Issue Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring—2nd Edition)
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16 pages, 4346 KiB  
Article
Winter Wheat Yield Estimation with Color Index Fusion Texture Feature
by Fuqin Yang, Yang Liu, Jiayu Yan, Lixiao Guo, Jianxin Tan, Xiangfei Meng, Yibo Xiao and Haikuan Feng
Agriculture 2024, 14(4), 581; https://doi.org/10.3390/agriculture14040581 - 06 Apr 2024
Viewed by 720
Abstract
The rapid and accurate estimation of crop yield is of great importance for large-scale agricultural production and national food security. Using winter wheat as the research object, the effects of color indexes, texture feature and fusion index on yield estimation were investigated based [...] Read more.
The rapid and accurate estimation of crop yield is of great importance for large-scale agricultural production and national food security. Using winter wheat as the research object, the effects of color indexes, texture feature and fusion index on yield estimation were investigated based on unmanned aerial vehicle (UAV) high-definition digital images, which can provide a reliable technical means for the high-precision yield estimation of winter wheat. In total, 22 visible color indexes were extracted using UAV high-resolution digital images, and a total of 24 texture features in red, green, and blue bands extracted by ENVI 5.3 were correlated with yield, while color indexes and texture features with high correlation and fusion indexes were selected to establish yield estimation models for flagging, flowering and filling stages using partial least squares regression (PLSR) and random forest (RF). The yield estimation model constructed with color indexes at the flagging and flowering stages, along with texture characteristics and fusion indexes at the filling stage, had the best accuracy, with R2 values of 0.70, 0.71 and 0.76 and RMSE values of 808.95 kg/hm2, 794.77 kg/hm2 and 728.85 kg/hm2, respectively. The accuracy of winter wheat yield estimation using PLSR at the flagging, flowering, and filling stages was better than that of RF winter wheat estimation, and the accuracy of winter wheat yield estimation using the fusion feature index was better than that of color and texture feature indexes; the distribution maps of yield results are in good agreement with those of the actual test fields. Thus, this study can provide a scientific reference for estimating winter wheat yield based on UAV digital images and provide a reference for agricultural farm management. Full article
(This article belongs to the Special Issue Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring—2nd Edition)
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18 pages, 13111 KiB  
Article
Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation
by Heguang Sun, Lin Zhou, Meiyan Shu, Jie Zhang, Ziheng Feng, Haikuan Feng, Xiaoyu Song, Jibo Yue and Wei Guo
Agriculture 2024, 14(3), 476; https://doi.org/10.3390/agriculture14030476 - 15 Mar 2024
Viewed by 704
Abstract
Southern blight significantly impacts peanut yield, and its severity is exacerbated by high-temperature and high-humidity conditions. The mycelium attached to the plant’s interior quickly proliferates, contributing to the challenges of early detection and data acquisition. In recent years, the integration of machine learning [...] Read more.
Southern blight significantly impacts peanut yield, and its severity is exacerbated by high-temperature and high-humidity conditions. The mycelium attached to the plant’s interior quickly proliferates, contributing to the challenges of early detection and data acquisition. In recent years, the integration of machine learning and remote sensing data has become a common approach for disease monitoring. However, the poor quality and imbalance of data samples can significantly impact the performance of machine learning algorithms. This study employed the Synthetic Minority Oversampling Technique (SMOTE) algorithm to generate samples with varying severity levels. Additionally, it utilized Fractional-Order Differentiation (FOD) to enhance spectral information. The validation and testing of the 1D-CNN, SVM, and KNN models were conducted using experimental data from two different locations. In conclusion, our results indicate that the SMOTE-FOD-1D-CNN model enhances the ability to monitor the severity of peanut white mold disease (validation OA = 88.81%, Kappa = 0.85; testing OA = 82.76%, Kappa = 0.75). Full article
(This article belongs to the Special Issue Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring—2nd Edition)
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17 pages, 17511 KiB  
Article
Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images
by Yan Guo, Jia He, Huifang Zhang, Zhou Shi, Panpan Wei, Yuhang Jing, Xiuzhong Yang, Yan Zhang, Laigang Wang and Guoqing Zheng
Agriculture 2024, 14(3), 378; https://doi.org/10.3390/agriculture14030378 - 27 Feb 2024
Viewed by 653
Abstract
Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In [...] Read more.
Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, by applying different water and nitrogen treatments, an unmanned aerial vehicle (UAV) equipped with a multispectral imaging spectrometer was used to acquire images of winter wheat during critical growth stages. Then, the plant height (Hdsm) extracted from the digital surface model (DSM) information was used to establish and improve the estimation model of AGB, using the backpropagation (BP) neural network, a machine learning method. The results show that (1) the R2, root-mean-square error (RMSE), and relative predictive deviation (RPD) of the AGB estimation model, constructed directly using the Hdsm, are 0.58, 4528.23 kg/hm2, and 1.25, respectively. The estimated mean AGB (16,198.27 kg/hm2) is slightly smaller than the measured mean AGB (16,960.23 kg/hm2). (2) The R2, RMSE, and RPD of the improved AGB estimation model, based on AGB/Hdsm, are 0.88, 2291.90 kg/hm2, and 2.75, respectively, and the estimated mean AGB (17,478.21 kg/hm2) is more similar to the measured mean AGB (17,222.59 kg/hm2). The improved AGB estimation model boosts the accuracy by 51.72% compared with the AGB directly estimated using the Hdsm. Moreover, the improved AGB estimation model shows strong transferability in regard to different water treatments and different year scenarios, but there are differences in the transferability for different N-level scenarios. (3) Differences in the characteristics of the data are the key factors that lead to the different transferability of the AGB estimation model. This study provides an antecedent in regard to model construction and transferability estimation of AGB for winter wheat. We confirm that, when different datasets have similar histogram characteristics, the model is applicable to new scenarios. Full article
(This article belongs to the Special Issue Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring—2nd Edition)
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