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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 7112

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Special Issue Editors

Dr. Haikuan Feng

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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
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Dr. Yanjun Yang

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Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
Interests: remote sensing; climate change; machine learning; ecosystem model
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Dr. Ning Zhang

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Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: UAV; smart orchard; pest management; pest risk mapping
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Dr. Chengquan Zhou

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Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
Interests: image segmentation; UAV; machine learning; pattern recognition; IOT
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Dr. Jibo Yue

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College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Interests: remote sensing; precision agriculture; machine learning; crop model; crop mapping
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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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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 (8 papers)

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Research

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 74

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, 1228 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 93

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)

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

Viewed by 535

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

Viewed by 1011

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

Viewed by 893

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

Viewed by 1173

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 975

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 2 | Viewed by 840

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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