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

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

Deadline for manuscript submissions: closed (25 June 2024) | Viewed by 15622

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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

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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

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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

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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

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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

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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

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Published Papers (14 papers)

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20 pages, 2351 KiB

Open AccessArticle

Combining UAV Multispectral Imaging and PROSAIL Model to Estimate LAI of Potato at Plot Scale

by Shuang Li, Yongxin Lin, Ping Zhu, Liping Jin, Chunsong Bian and Jiangang Liu

Agriculture 2024, 14(12), 2159; https://doi.org/10.3390/agriculture14122159 - 27 Nov 2024

Abstract

Accurate and rapid estimation of the leaf area index (LAI) is essential for assessing crop growth and nutritional status, guiding farm management, and providing valuable phenotyping data for plant breeding. Compared to the tedious and time-consuming manual measurements of the LAI, remote sensing has emerged as a valuable tool for rapid and accurate estimation of the LAI; however, the empirical inversion modeling methods face challenges of low efficiency for actual LAI measurements and poor model interpretability. The integration of radiative transfer models (RTMs) can overcome these problems to some extent. The aim of this study was to explore the potential of combining the PROSAIL model with high-resolution unmanned aerial vehicle (UAV) multispectral imaging to estimate the LAI across different growth stages at the plot scale. In this study, four inversion strategies for estimating the LAI were tested. Firstly, two types of lookup tables (LUTs) were built to estimate potato LAI of different varieties across different growth stages. Specifically, LUT1 was based on band reflectance, and LUT2 was based on vegetation index. Secondly, the hybrid models combining LUTs generated by PROSAIL and two machine learning algorithms (random forest (RF), Partial Least Squares Regression (PLSR)) are built to estimate potato LAI. The determination of coefficient (R²) of models for estimating LAI by LUTs ranged from 0.24 to 0.64. The hybrid method that integrates UAV multispectral, PROSAIL, and machine learning significantly improved the accuracy of LAI estimation. Compared to the results based on LUT2, the hybrid model achieved higher accuracy with the R² of the inversion model improved by 30% to 263%. The LAI retrieval model using the PROSAIL model and PLSR achieved an R²as high as 0.87, while the R² using the RF algorithm ranged from 0.33 to 0.81. The proposed hybrid model, integrated with UAV multispectral data, PROSAIL, and PLSR can achieve approximate accuracy compared with the empirical inversion models, which can alleviate the labor-intensive process of handheld LAI measurements for building inversion models. Thus, the hybrid approach provides a feasible and efficient strategy for estimating the LAI of potato varieties across different growth stages at the plot scale. Full article

(This article belongs to the Special Issue Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring—2nd Edition)

16 pages, 3804 KiB

Open AccessArticle

Detection of Mechanical Damage in Corn Seeds Using Hyperspectral Imaging and the ResNeSt_E Deep Learning Network

by Hua Huang, Yinfeng Liu, Shiping Zhu, Chuan Feng, Shaoqi Zhang, Lei Shi, Tong Sun and Chao Liu

Agriculture 2024, 14(10), 1780; https://doi.org/10.3390/agriculture14101780 - 10 Oct 2024

Viewed by 657

Abstract

Corn is one of the global staple grains and the largest grain crop in China. During harvesting, grain separation, and corn production, corn is susceptible to mechanical damage including surface cracks, internal cracks, and breakage. However, the internal cracks are difficult to observe. In this study, hyperspectral imaging was used to detect mechanical damage in corn seeds. The corn seeds were divided into four categories that included intact, broken, internally cracked, and surface-crackedtv. This study compared three feature extraction methods, including principal component analysis (PCA), kernel PCA (KPCA), and factor analysis (FA), as well as a joint feature extraction method consisting of a combination of these methods. The dimensionality reduction results of the three methods (FA + KPCA, KPCA + FA, and PCA + FA) were combined to form a new combined dataset and improve the classification. We then compared the effects of six classification models (ResNet, ShuffleNet-V2, MobileNet-V3, ResNeSt, EfficientNet-V2, and MobileNet-V4) and proposed a ResNeSt_E network based on the ResNeSt and efficient multi-scale attention modules. The accuracy of ResNeSt_E reached 99.0%, and this was 0.4% higher than that of EfficientNet-V2 and 0.7% higher than that of ResNeSt. Additionally, the number of parameters and memory requirements were reduced and the frames per second were improved. We compared two dimensionality reduction methods: KPCA + FA and PCA + FA. The classification accuracies of the two methods were the same; however, PCA + FA was much more efficient than KPCA + FA and was more suitable for practical detection. The ResNeSt_E network could detect both internal and surface cracks in corn seeds, making it suitable for mobile terminal applications. The results demonstrated that detecting mechanical damage in corn seeds using hyperspectral images was possible. This study provides a reference for mechanical damage detection methods for corn. 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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19 pages, 4569 KiB

Open AccessArticle

Effects of Variety and Growth Stage on UAV Multispectral Estimation of Plant Nitrogen Content of Winter Wheat

by Meiyan Shu, Zhiyi Wang, Wei Guo, Hongbo Qiao, Yuanyuan Fu, Yan Guo, Laigang Wang, Yuntao Ma and Xiaohe Gu

Agriculture 2024, 14(10), 1775; https://doi.org/10.3390/agriculture14101775 - 9 Oct 2024

Viewed by 720

Abstract

The accurate estimation of nitrogen content in crop plants is the basis of precise nitrogen fertilizer management. Unmanned aerial vehicle (UAV) imaging technology has been widely used to rapidly estimate the nitrogen in crop plants, but the accuracy will still be affected by the variety, the growth stage, and other factors. We aimed to (1) analyze the correlation between the plant nitrogen content of winter wheat and spectral, texture, and structural information; (2) compare the accuracy of nitrogen estimation at single versus multiple growth stages; (3) assess the consistency of UAV multispectral images in estimating nitrogen content across different wheat varieties; (4) identify the best model for estimating plant nitrogen content (PNC) by comparing five machine learning algorithms. The results indicated that for the estimation of PNC across all varieties and growth stages, the random forest regression (RFR) model performed best among the five models, obtaining R², RMSE, MAE, and MAPE values of 0.90, 0.10%, 0.08, and 0.06%, respectively. Additionally, the RFR estimation model achieved commendable accuracy in estimating PNC in three different varieties, with R² values of 0.91, 0.93, and 0.72. For the dataset of the single growth stage, Gaussian process regression (GPR) performed best among the five regression models, with R² values ranging from 0.66 to 0.81. Due to the varying nitrogen sensitivities, the accuracy of UAV multispectral nitrogen estimation was also different among the three varieties. Among the three varieties, the estimation accuracy of SL02-1 PNC was the worst. This study is helpful for the rapid diagnosis of crop nitrogen nutrition through UAV multispectral imaging technology. 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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19 pages, 11653 KiB

Open AccessArticle

Influence of Vegetation Phenology on the Temporal Effect of Crop Fractional Vegetation Cover Derived from Moderate-Resolution Imaging Spectroradiometer Nadir Bidirectional Reflectance Distribution Function–Adjusted Reflectance

by Yinghao Lin, Tingshun Fan, Dong Wang, Kun Cai, Yang Liu, Yuye Wang, Tao Yu and Nianxu Xu

Agriculture 2024, 14(10), 1759; https://doi.org/10.3390/agriculture14101759 - 5 Oct 2024

Viewed by 662

Abstract

Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product. 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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19 pages, 6791 KiB

Open AccessArticle

Vegetation Phenology Changes and Recovery after an Extreme Rainfall Event: A Case Study in Henan Province, China

by Yinghao Lin, Xiaoyu Guo, Yang Liu, Liming Zhou, Yadi Wang, Qiang Ge and Yuye Wang

Agriculture 2024, 14(9), 1649; https://doi.org/10.3390/agriculture14091649 - 20 Sep 2024

Viewed by 498

Abstract

Extreme rainfall can severely affect all vegetation types, significantly impacting crop yield and quality. This study aimed to assess the response and recovery of vegetation phenology to an extreme rainfall event (with total weekly rainfall exceeding 500 mm in several cities) in Henan Province, China, in 2021. The analysis utilized multi-sourced data, including remote sensing reflectance, meteorological, and crop yield data. First, the Normalized Difference Vegetation Index (NDVI) time series was calculated from reflectance data on the Google Earth Engine (GEE) platform. Next, the ‘phenofit’ R language package was used to extract the phenology parameters—the start of the growing season (SOS) and the end of the growing season (EOS). Finally, the Statistical Package for the Social Sciences (SPSS, v.26.0.0.0) software was used for Duncan’s analysis, and Matrix Laboratory (MATLAB, v.R2022b) software was used to analyze the effects of rainfall on land surface phenology (LSP) and crop yield. The results showed the following. (1) The extreme rainfall event’s impact on phenology manifested directly as a delay in EOS in the year of the event. In 2021, the EOS of the second growing season was delayed by 4.97 days for cropland, 15.54 days for forest, 13.06 days for grassland, and 12.49 days for shrubland. (2) Resistance was weak in 2021, but recovery reached in most areas by 2022 and slowed in 2023. (3) In each year, SOS was predominantly negatively correlated with total rainfall in July (64% of cropland area in the first growing season, 53% of grassland area, and 71% of shrubland area). In contrast, the EOS was predominantly positively correlated with rainfall (51% and 54% area of cropland in the first and second growing season, respectively, and 76% of shrubland area); however, crop yields were mainly negatively correlated with rainfall (71% for corn, 60% for beans) and decreased during the year of the event, with negative correlation coefficients between rainfall and yield (−0.02 for corn, −0.25 for beans). This work highlights the sensitivity of crops to extreme rainfall and underscores the need for further research on their long-term recovery. 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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23 pages, 9401 KiB

Open AccessArticle

Refinement of Cropland Data Layer with Effective Confidence Layer Interval and Image Filtering

by Reza Maleki, Falin Wu, Amel Oubara, Loghman Fathollahi and Gongliu Yang

Agriculture 2024, 14(8), 1285; https://doi.org/10.3390/agriculture14081285 - 4 Aug 2024

Viewed by 959

Abstract

Various systems have been developed to process agricultural land data for better management of crop production. One such system is Cropland Data Layer (CDL), produced by the National Agricultural Statistics Service of the United States Department of Agriculture (USDA). The CDL has been widely used for training deep learning (DL) segmentation models. However, it contains various errors, such as salt-and-pepper noise, and must be refined before being used in DL training. In this study, we used two approaches to refine the CDL for DL segmentation of major crops from a time series of Sentinel-2 monthly composite images. Firstly, different confidence intervals of the confidence layer were used to refine the CDL. Secondly, several image filters were employed to improve data quality. The refined CDLs were then used as the ground-truth in DL segmentation training and evaluation. The results demonstrate that the CDL with +45% and +55% confidence intervals produced the best results, improving the accuracy of DL segmentation by approximately 1% compared to non-refined data. Additionally, filtering the CDL using the majority and expand–shrink filters yielded the best performance, enhancing the evaluation metrics by about 1.5%. The findings suggest that pre-filtering the CDL and selecting an effective confidence interval can significantly improve DL segmentation performance, contributing to more accurate and reliable agricultural monitoring. 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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19 pages, 6138 KiB

Open AccessArticle

Spectral-Frequency Conversion Derived from Hyperspectral Data Combined with Deep Learning for Estimating Chlorophyll Content in Rice

by Lei Du and Shanjun Luo

Agriculture 2024, 14(7), 1186; https://doi.org/10.3390/agriculture14071186 - 18 Jul 2024

Viewed by 1011

Abstract

As a vital pigment for photosynthesis in rice, chlorophyll content is closely correlated with growth status and photosynthetic capacity. The estimation of chlorophyll content allows for the monitoring of rice growth and facilitates precise management in the field, such as the application of fertilizers and irrigation. The advancement of hyperspectral remote sensing technology has made it possible to estimate chlorophyll content non-destructively, quickly, and effectively, offering technical support for managing and monitoring rice growth across wide areas. Although hyperspectral data have a fine spectral resolution, they also cause a large amount of information redundancy and noise. This study focuses on the issues of unstable input variables and the estimation model’s poor applicability to various periods when predicting rice chlorophyll content. By introducing the theory of harmonic analysis and the time-frequency conversion method, a deep neural network (DNN) model framework based on wavelet packet transform-first order differential-harmonic analysis (WPT-FD-HA) was proposed, which avoids the uncertainty in the calculation of spectral parameters. The accuracy of estimating rice chlorophyll content based on WPT-FD and WPT-FD-HA variables was compared at seedling, tillering, jointing, heading, grain filling, milk, and complete periods to evaluate the validity and generalizability of the suggested framework. The results demonstrated that all of the WPT-FD-HA models’ single-period validation accuracy had coefficients of determination (R²) values greater than 0.9 and RMSE values less than 1. The multi-period validation model had a root mean square error (RMSE) of 1.664 and an R² of 0.971. Even with independent data splitting validation, the multi-period model accuracy can still achieve R² = 0.95 and RMSE = 1.4. The WPT-FD-HA-based deep learning framework exhibited strong stability. The outcome of this study deserves to be used to monitor rice growth on a broad scale using hyperspectral data. 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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21 pages, 8463 KiB

Open AccessArticle

Rapid Detection of Fertilizer Information Based on Near-Infrared Spectroscopy and Machine Learning and the Design of a Detection Device

by Yongzheng Ma, Zhuoyuan Wu, Yingying Cheng, Shihong Chen and Jianian Li

Agriculture 2024, 14(7), 1184; https://doi.org/10.3390/agriculture14071184 - 18 Jul 2024

Viewed by 1049

Abstract

The online detection of fertilizer information is pivotal for precise and intelligent variable-rate fertilizer application. However, traditional methods face challenges such as the complex quantification of multiple components and sensor-induced cross-contamination. This study investigates integrating near-infrared principles with machine learning algorithms to identify fertilizer types and concentrations. We utilized near-infrared transmission spectroscopy and applied Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Back-Propagation Neural Network (BPNN) algorithms to analyze full spectrum data. The BPNN model, using S-G smoothing, demonstrated a superior classification performance for the nutrient ions of four fertilizer solutions: HPO₄²⁻, NH₄⁺, H₂PO₄⁻ and K⁺. Optimization using the competitive adaptive reweighted sampling (CARS) method yielded BPNN model RMSE values of 0.3201, 0.7160, 0.2036, and 0.0177 for HPO₄²⁻, NH₄⁺, H₂PO₄⁻, and K⁺, respectively. Building on this foundation, we designed a four-channel fertilizer detection device based on the Lambert–Beer law, enabling the real-time detection of fertilizer types and concentrations. The test results confirmed the device’s robust stability, achieving 93% accuracy in identifying fertilizer types and concentrations, with RMSE values ranging from 1.0034 to 2.4947, all within ±8.0% error margin. This study addresses the practical requirements for online fertilizer detection in agricultural engineering, laying the groundwork for efficient water–fertilizer integration technology aligned with sustainable development goals. 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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14 pages, 15703 KiB

Open AccessArticle

High-Precision Peach Fruit Segmentation under Adverse Conditions Using Swin Transformer

by Dasom Seo, Seul Ki Lee, Jin Gook Kim and Il-Seok Oh

Agriculture 2024, 14(6), 903; https://doi.org/10.3390/agriculture14060903 - 7 Jun 2024

Cited by 1 | Viewed by 968

Abstract

In the realm of agricultural automation, the efficient management of tasks like yield estimation, harvesting, and monitoring is crucial. While fruits are typically detected using bounding boxes, pixel-level segmentation is essential for extracting detailed information such as color, maturity, and shape. Furthermore, while previous studies have typically focused on controlled environments and scenes, achieving robust performance in real orchard conditions is also imperative. To prioritize these aspects, we propose the following two considerations: first, a novel peach image dataset designed for rough orchard environments, focusing on pixel-level segmentation for detailed insights; and second, utilizing a transformer-based instance segmentation model, specifically the Swin Transformer as a backbone of Mask R-CNN. We achieve superior results compared to CNN-based models, reaching 60.2 AP on the proposed peach image dataset. The proposed transformer-based approach specially excels in detecting small or obscured peaches, making it highly suitable for practical field applications. The proposed model achieved 40.4 AP for small objects, nearly doubling that of CNN-based models. This advancement significantly enhances automated agricultural systems, especially in yield estimation, harvesting, and crop monitoring. 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, 2107 KiB

Open AccessArticle

Phenotyping the Anthocyanin Content of Various Organs in Purple Corn Using a Digital Camera

by Zhengxin Wang, Ye Liu, Ke Wang, Yusong Wang, Xue Wang, Jiaming Liu, Cheng Xu and Youhong Song

Agriculture 2024, 14(5), 744; https://doi.org/10.3390/agriculture14050744 - 10 May 2024

Cited by 1 | Viewed by 1636

Abstract

Anthocyanins are precious industrial raw materials. Purple corn is rich in anthocyanins, with large variation in their content between organs. It is imperative to find a rapid and non-destructive method to determine the anthocyanin content in purple corn. To this end, a field experiment with ten purple corn hybrids was conducted, collecting plant images using a digital camera and determining the anthocyanin content of different organ types. The average values of red (R), green (G) and blue (B) in the images were extracted. The color indices derived from RGB arithmetic operations were applied in establishing a model for estimation of the anthocyanin content. The results showed that the specific color index varied with the organ type in purple corn, i.e., AC_CR for the grains, BRT for the cobs, AC_CB for the husks, R for the stems, AC_CB for the sheaths and BRT for the laminae, respectively. Linear models of the relationship between the color indices and anthocyanin content for different organs were established with R² falling in the range of 0.64–0.94. The predictive accuracy of the linear models, assessed according to the NRMSE, was validated using a sample size of 2:1. The average NRMSE value was 11.68% in the grains, 13.66% in the cobs, 8.90% in the husks, 27.20% in the stems, 7.90% in the sheaths and 15.83% in the laminae, respectively, all less than 30%, indicating that the accuracy and stability of the model was trustworthy and reliable. In conclusion, this study provided a new method for rapid, non-destructive prediction of anthocyanin-rich organs in purple corn. 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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20 pages, 4620 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1358

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 (VI_opt) 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 (VI_opt) and the corresponding VI_DRI (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 VI_DRI indexes were more beneficial than the VI_opt indexes in enhancing the estimation accuracy of corn growth parameters. Specifically, the nRMSEs of the four VI_DRI indexes (NDVI_DRI, NDVIre1_DRI, NDVIre2_DRI, and S2REP_DRI) 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 NDVIre2_DRI and S2REP_DRI 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

Open AccessArticle

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 - 6 Apr 2024

Cited by 2 | Viewed by 1601

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1304

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 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

Open AccessArticle

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

Cited by 3 | Viewed by 1173

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 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 (H_dsm) 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 H_dsm, 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/H_dsm, 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 H_dsm. 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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