Sensors Applied to Agricultural Products

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

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 50327

Special Issue Editors

College of Engineering, China Agricultural University, Beijing 100083, China
Interests: hyperspectral imaging; machine vision; near infrared spectroscopy; nondestrctive detection; sorting; instruments and equipment
Special Issues, Collections and Topics in MDPI journals
Institute of Quality Standard and Testing Technology for Agro-products, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: nanomaterials; biosensors; immunoassay
Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China
Interests: nanobodies; mycotoxins; rapid detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The quality and safety of agricultural products directly affects human nutrition and health, quality and safety of livestock, and poultry and aquatic products, which has become a hot widely concerned social problem. Sensing and detecting techniques are used to obtain information such as physical characteristics, chemical composition, content, and distribution, as well as hazardous substances of the target so as to ensure the quality and safety of agricultural products. Today, with the development of new materials, advanced software algorithms, artificial intelligence, and big data, together with interdisciplinary of biology, chemistry, physics, etc., modern sensing technology is moving toward online, in situ, nondestructive versions that offer high speed and efficiency, ultra-miniaturization and high integration, high throughput, and intelligence. These emerging technologies, such as any kind of optical, acoustic, spectroscopy, electromagnetism, biology, and electrochemical sensors, together with classical sensing methods, will greatly promote the rapid development of agricultural product quality and safety, so as to better serve life and health.

Agriculture provides an advanced forum for the S&T of sensors and their specific applications in agricultural products, including quality control of agricultural products in all stages of picking, slaughter and fishing, harvesting, sorting, processing, transportation, storage, and so on. This Special Issue publishes original papers and reviews on topics pertaining to advances in wide applications of sensors in agriculture, including but not limited to cereals, oils, fruits, vegetables, eggs, meat, dairy and fishery products, as well as all kinds of feed. Relevant areas of technology include artificial intelligence, sensors, spectral detection, machine vision, acoustic characteristics analysis, electromagnetic properties analysis, X-ray analysis, and simulation modelling. Additionally, interdisciplinary research and application research related to sensors applied to agricultural products is also encouraged.

Prof. Dr. Wei Wang
Dr. Seung-Chul Yoon
Dr. Peilong Wang
Dr. Xiaoqian Tang
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

  • agricultural products
  • food control
  • quality assurance
  • food processing
  • sensors
  • nondestructive
  • detection

Published Papers (19 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

10 pages, 590 KiB  
Article
Simultaneous Determination of γ-Oryzanol in Agriproducts by Solid-Phase Extraction Coupled with UHPLC–MS/MS
by Lei Lv, Liangxiao Zhang, Mengxiang Gao and Fei Ma
Agriculture 2023, 13(3), 531; https://doi.org/10.3390/agriculture13030531 - 22 Feb 2023
Viewed by 1183
Abstract
In this work, a simple, rapid and cost-effective method for the simultaneous quantification of two major γ-oryzanol components in agriproducts was established by silica solid-phase extraction (SPE) coupled with UHPLC–MS/MS. Silica SPE sorbents consist of unbonded silica gel with high polarity and can [...] Read more.
In this work, a simple, rapid and cost-effective method for the simultaneous quantification of two major γ-oryzanol components in agriproducts was established by silica solid-phase extraction (SPE) coupled with UHPLC–MS/MS. Silica SPE sorbents consist of unbonded silica gel with high polarity and can retain most of the analytes with acidic properties. Silica sorbents are cost-effective materials and that can be prepared simply without a large volume of toxic chlorinated solvent. Silica SPE sorbents were utilized to extract and purify cycloartenyl ferulate (CF) and 24-methylene cycloartanyl ferulate (24-CF) in cereal products. Various parameters affecting the isolation recoveries were studied. By coupling with ultra-high-performance liquid chromatography–mass spectrometry (UHPLC–MS/MS), a novel method for the quantification of CF and 24-CF in agriproducts was developed and validated. The procedure used silica sorbent to purify the analytes in 30 min without complicated steps, which improved the simplicity and efficiency. The limits of quantification and the limits of detection of CF and 24-CF were 0.3 and 1.0 μg kg−1, respectively. Extraction recoveries ranged from 86.93% to 108.75% with inter-day and intra-day precisions less than 10.84%. The results of 50 agriproducts indicated that the rice bran had the highest averaged amount of 34.3 × 103 μg kg−1 for CF and 42.6 × 103 μg kg−1 for 24-CF, making it a perfect source of human nutritional supplement substances from agriproducts. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

19 pages, 11441 KiB  
Article
Recognition and Positioning of Fresh Tea Buds Using YOLOv4-lighted + ICBAM Model and RGB-D Sensing
by Shudan Guo, Seung-Chul Yoon, Lei Li, Wei Wang, Hong Zhuang, Chaojie Wei, Yang Liu and Yuwen Li
Agriculture 2023, 13(3), 518; https://doi.org/10.3390/agriculture13030518 - 21 Feb 2023
Cited by 5 | Viewed by 1710
Abstract
To overcome the low recognition accuracy, slow speed, and difficulty in locating the picking points of tea buds, this paper is concerned with the development of a deep learning method, based on the You Only Look Once Version 4 (YOLOv4) object detection algorithm, [...] Read more.
To overcome the low recognition accuracy, slow speed, and difficulty in locating the picking points of tea buds, this paper is concerned with the development of a deep learning method, based on the You Only Look Once Version 4 (YOLOv4) object detection algorithm, for the detection of tea buds and their picking points with tea-picking machines. The segmentation method, based on color and depth data from a stereo vision camera, is proposed to detect the shapes of tea buds in 2D and 3D spaces more accurately than using 2D images. The YOLOv4 deep learning model for object detection was modified to obtain a lightweight model with a shorter inference time, called YOLOv4-lighted. Then, Squeeze-and-Excitation Networks (SENet), Efficient Channel Attention (ECA), Convolutional Block Attention Module (CBAM), and improved CBAM (ICBAM) were added to the output layer of the feature extraction network, for improving the detection accuracy of tea features. Finally, the Path Aggregation Network (PANet) in the neck network was simplified to the Feature Pyramid Network (FPN). The light-weighted YOLOv4 with ICBAM, called YOLOv4-lighted + ICBAM, was determined as the optimal recognition model for the detection of tea buds in terms of accuracy (94.19%), recall (93.50%), F1 score (0.94), and average precision (97.29%). Compared with the baseline YOLOv4 model, the size of the YOLOv4-lighted + ICBAM model decreased by 75.18%, and the frame rate increased by 7.21%. In addition, the method for predicting the picking point of each detected tea bud was developed by segmentation of the tea buds in each detected bounding box, with filtering of each segment based on its depth from the camera. The test results showed that the average positioning success rate and the average positioning time were 87.10% and 0.12 s, respectively. In conclusion, the recognition and positioning method proposed in this paper provides a theoretical basis and method for the automatic picking of tea buds. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

20 pages, 4960 KiB  
Article
Detection and Correction of Abnormal IoT Data from Tea Plantations Based on Deep Learning
by Ruiqing Wang, Jinlei Feng, Wu Zhang, Bo Liu, Tao Wang, Chenlu Zhang, Shaoxiang Xu, Lifu Zhang, Guanpeng Zuo, Yixi Lv, Zhe Zheng, Yu Hong and Xiuqi Wang
Agriculture 2023, 13(2), 480; https://doi.org/10.3390/agriculture13020480 - 17 Feb 2023
Cited by 1 | Viewed by 1408
Abstract
This paper proposes a data anomaly detection and correction algorithm for the tea plantation IoT system based on deep learning, aiming at the multi-cause and multi-feature characteristics of abnormal data. The algorithm is based on the Z-score standardization of the original data and [...] Read more.
This paper proposes a data anomaly detection and correction algorithm for the tea plantation IoT system based on deep learning, aiming at the multi-cause and multi-feature characteristics of abnormal data. The algorithm is based on the Z-score standardization of the original data and the determination of sliding window size according to the sampling frequency. First, we construct a convolutional neural network (CNN) model to extract abnormal data. Second, based on the support vector machine (SVM) algorithm, the Gaussian radial basis function (RBF) and one-to-one (OVO) multiclassification method are used to classify the abnormal data. Then, after extracting the time points of abnormal data, a long short-term memory network is established for prediction with multifactor historical data. The predicted values are used to replace and correct the abnormal data. When multiple consecutive abnormal values are detected, a faulty sensor judgment is given, and the specific faulty sensor location is output. The results show that the accuracy rate and micro-specificity of abnormal data detection for the CNN-SVM model are 3–4% and 20–30% higher than those of the traditional CNN model, respectively. The anomaly detection and correction algorithm for tea plantation data established in this paper provides accurate performance. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

17 pages, 5019 KiB  
Article
Design and Experiment of Feeding Device for Hairy Vetch Harvesting
by Wei Wang, Jiahang Li, Shuoming Wang, Lei Li, Lin Yuan, Shiqiang Yv, Jun Zhang, Junming Hou and Ren Zhang
Agriculture 2023, 13(2), 345; https://doi.org/10.3390/agriculture13020345 - 31 Jan 2023
Viewed by 1502
Abstract
In order to solve the problem of low mechanization level of hairy vetch harvesting, a feeding device for an anti-winding hairy vetch harvester was designed. Firstly, the physical properties of hairy vetch stalk were studied. According to the mechanical properties of hairy vetch [...] Read more.
In order to solve the problem of low mechanization level of hairy vetch harvesting, a feeding device for an anti-winding hairy vetch harvester was designed. Firstly, the physical properties of hairy vetch stalk were studied. According to the mechanical properties of hairy vetch stalk, the key components of the feeding device were designed and analyzed, the structure and working principle of the feeding device were described. Secondly, the discrete element method was used to simulate and analyze the movement performance of the feeding device. On this basis, the ternary quadratic regression orthogonal rotation combination test was established with the vertical drum, the machine forward speed, and the spiral conveyor speed as the test factors and the stem loss rate as the test index. The simulation results showed that when the vertical drum was 1037.5 r/min, the machine forward speed was 2.76 m/s, the spiral conveyor speed was 348.88 r/min, and the straw loss rate was 2.38%, and the feeding device performs best at this time. Finally, the on-site performance test of the feeding device was carried out. The results showed that: all the test indicators met the requirements of the national standard; the actual cutting width was 1.66 m; the cutting stubble height was 6.41 mm; the over stubble loss rate was 0.45%; the missed cutting loss rate was 0.20%; and the stem loss rate was 3.00%, which verified the rationality of the design of the feeding device. In order to solve the problem of low mechanization level of hairy vetch, which easily becomes entangled in the working process, an anti-winding feeding device for hairy vetch harvesting was designed. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

13 pages, 3471 KiB  
Article
Evaluation of Aspergillus flavus Growth and Detection of Aflatoxin B1 Content on Maize Agar Culture Medium Using Vis/NIR Hyperspectral Imaging
by Xiaohuan Guo, Beibei Jia, Haicheng Zhang, Xinzhi Ni, Hong Zhuang, Yao Lu and Wei Wang
Agriculture 2023, 13(2), 237; https://doi.org/10.3390/agriculture13020237 - 19 Jan 2023
Cited by 1 | Viewed by 1560
Abstract
The physiological and biochemical processes of Aspergillus flavus (A. flavus) are complex. Monitoring the metabolic evolution of products during the growth of A. flavus is critical to the overall understanding of the fungal and aflatoxin production detection mechanism. The dynamic growth [...] Read more.
The physiological and biochemical processes of Aspergillus flavus (A. flavus) are complex. Monitoring the metabolic evolution of products during the growth of A. flavus is critical to the overall understanding of the fungal and aflatoxin production detection mechanism. The dynamic growth process of A. flavus and the aflatoxin B1 (AFB1) accumulation in culture media was investigated with a visible/near-infrared hyperspectral imaging (Vis/NIR HSI) system in the range of 400 to 1000 nm. First, the growth of A. flavus and the synthesis pattern of AFB1 were monitored on maize agar medium (MAM) culture for 120 h with a 24-h time-lapse imaging interval. Second, to classify the A. flavus growth, a principal component analysis (PCA) was employed, and a support vector machine (SVM) model was established with the PC1–PC3 as inputs. The results suggested that the PCA-SVM method could distinguish the A. flavus growth time with a classification accuracy larger than 0.97, 0.91, and 0.92 for calibration, validation, and cross-validation, respectively. Third, regression models to predict the AFB1 accumulation using hyperspectral images were developed by comparing different pre-processing methods and key wavelengths. The successive projection algorithm (SPA) was adopted to distill the key wavelengths. The experimental results indicated that the standard normal variate transformation (SNV) with the partial least squares regression (PLSR) achieved the optimal regression performance with an RC value of 0.98–0.99 for calibration and RV values of 0.95–0.96 for validation. Finally, a spatial map of the AFB1 concentration was created using the PLSR model. The spatial regularity of the AFB1 concentration was comparable to the measurement performed. The study proved the potential of the Vis/NIR HSI to characterize the A. flavus growth and the concentration of AFB1 on the MAM over time. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

16 pages, 3912 KiB  
Article
The Use of Hough Transform Method and Knot-Like Turning for Motion Planning and Control of an Autonomous Agricultural Vehicle
by Gokhan Bayar
Agriculture 2023, 13(1), 92; https://doi.org/10.3390/agriculture13010092 - 29 Dec 2022
Cited by 1 | Viewed by 1412
Abstract
This study focuses on motion planning and reference trajectory tracking control of an autonomous agricultural vehicle to achieve precise row following and turning. The smooth time-varying feedback control method was adapted to the system to generate the required control commands. The mathematical representations [...] Read more.
This study focuses on motion planning and reference trajectory tracking control of an autonomous agricultural vehicle to achieve precise row following and turning. The smooth time-varying feedback control method was adapted to the system to generate the required control commands. The mathematical representations for motion planning and controllers were constructed based on the car-like robot model. An algorithm to detect trees and rows of trees of an orchard was developed using the Hough transform approach. A new type of turning procedure, called knot-like turning, was proposed to perform turning from one row to another. A simulation environment was created to test and analyze the developed system. To obtain the real data from a field, the trees and rows of trees of a cherry orchard were scanned using a laser scanner rangefinder sensor. Then, the scanned data were moved to the simulation environment to generate the desired trajectory, which was followed by an autonomous agricultural vehicle. The simulation environment made it possible to determine the performance of the proposed motion planning, reference trajectory generation, tracking control and turning procedures. The results presented here indicate that the proposed methodology could be used for desired trajectory tracking tasks for agricultural operations in the case that minimum tracking errors in both straight and turning motions are needed. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

21 pages, 6888 KiB  
Article
Detection of Water Content in Lettuce Canopies Based on Hyperspectral Imaging Technology under Outdoor Conditions
by Jing Zhao, Hong Li, Chao Chen, Yiyuan Pang and Xiaoqing Zhu
Agriculture 2022, 12(11), 1796; https://doi.org/10.3390/agriculture12111796 - 28 Oct 2022
Cited by 2 | Viewed by 1502
Abstract
To solve the problem of non-destructive crop water content of detection under outdoor conditions, we propose a method to predict lettuce canopy water content by collecting outdoor hyperspectral images of potted lettuce plants and combining spectral analysis techniques and model training methods. Firstly, [...] Read more.
To solve the problem of non-destructive crop water content of detection under outdoor conditions, we propose a method to predict lettuce canopy water content by collecting outdoor hyperspectral images of potted lettuce plants and combining spectral analysis techniques and model training methods. Firstly, background noise was removed by correlation segmentation, proposed in this paper, whereby light intensity correction is performed on the segmented lettuce canopy images. We then chose the first derivative combined with mean centering (MC) to preprocess the raw spectral data. Hereafter, feature bands were screened by a combination of Monte Carlo uninformative variable elimination (MCUVE) and competitive adaptive reweighting sampling (CARS) to eliminate redundant information. Finally, a lettuce canopy moisture prediction model was constructed by combining partial least squares (PLS). The correlation coefficient between model predicted and measured values was used as the main model performance evaluation index, and the modeling set correlation coefficient Rc was 82.71%, while the prediction set correlation coefficient RP was 84.67%. The water content of each lettuce canopy pixel was calculated by the constructed model, and the visualized lettuce water distribution map was generated by pseudo-color image processing, which finally revealed a visualization of the water content of the lettuce canopy leaves under outdoor conditions. This study extends the hyperspectral image prediction possibilities of lettuce canopy water content under outdoor conditions. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

16 pages, 2075 KiB  
Article
Continuous Wavelet Transform and Back Propagation Neural Network for Condition Monitoring Chlorophyll Fluorescence Parameters Fv/Fm of Rice Leaves
by Shuangya Wen, Nan Shi, Junwei Lu, Qianwen Gao, Wenrui Hu, Zhengdengyuan Cao, Jianxiang Lu, Huibin Yang and Zhiqiang Gao
Agriculture 2022, 12(8), 1197; https://doi.org/10.3390/agriculture12081197 - 11 Aug 2022
Cited by 9 | Viewed by 1912
Abstract
The chlorophyll fluorescence parameter Fv/Fm (maximum photosynthetic efficiency of optical system II) is an intrinsic index for exploring plant photosynthesis. Hyperspectral remote sensing technology can be used for rapid nondestructive detection of chlorophyll fluorescence parameters. Existing studies show that there is a good [...] Read more.
The chlorophyll fluorescence parameter Fv/Fm (maximum photosynthetic efficiency of optical system II) is an intrinsic index for exploring plant photosynthesis. Hyperspectral remote sensing technology can be used for rapid nondestructive detection of chlorophyll fluorescence parameters. Existing studies show that there is a good correlation between the vegetation index and Fv/Fm. However, due to the limited hyperspectral information reflected by the vegetation index, the established model often cannot reach the ideal accuracy. Therefore, this study took rice as the research object and explored the internal relationship between chlorophyll fluorescence parameters and spectral reflectance by setting different fertilization treatments. Spectral sensitive information was extracted by vegetation index and continuous wavelet transform (CWT) to explore a more suitable method for Fv/Fm hyperspectral estimation at the rice leaf scale. Then a monitoring model of Fv/Fm in rice leaves was established by the back propagation neural network (BPNN) algorithm. The results showed that: (1) the accuracy of univariate models constructed by Fv/Fm inversion based on 10 commonly used vegetation indices constructed by traditional methods was low; (2) The correlation between leaf hyperspectral reflectance and Fv/Fm could be effectively improved by using CWT, and the accuracy of the univariate model constructed by using the best wavelet coefficients could reach the level of rough evaluation of Fv/Fm; (3) The effect of wavelet transform using different mother wavelet functions as the basis function was different, and bior3.3 function was the best; R2, RMSE and RPD of the BPNN model constructed by using the first 10 best wavelet coefficients decomposed by the bior3.3 was 0.823 6, 0.013 2 and 2.304 3. In conclusion, this study proves that CWT can effectively extract sensitive bands of rice leaves for Fv/Fm monitoring, providing a reference for the follow-up rapid and nondestructive monitoring of chlorophyll fluorescence. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

18 pages, 30348 KiB  
Article
Green Banana Maturity Classification and Quality Evaluation Using Hyperspectral Imaging
by Xuan Chu, Pu Miao, Kun Zhang, Hongyu Wei, Han Fu, Hongli Liu, Hongzhe Jiang and Zhiyu Ma
Agriculture 2022, 12(4), 530; https://doi.org/10.3390/agriculture12040530 - 08 Apr 2022
Cited by 23 | Viewed by 6516
Abstract
Physiological maturity of bananas is of vital importance in determination of their quality and marketability. This study assessed, with the use of a Vis/NIR hyperspectral imaging (400–1000 nm), the feasibility in differentiating six maturity levels (maturity level 2, 4, and 6 to 9) [...] Read more.
Physiological maturity of bananas is of vital importance in determination of their quality and marketability. This study assessed, with the use of a Vis/NIR hyperspectral imaging (400–1000 nm), the feasibility in differentiating six maturity levels (maturity level 2, 4, and 6 to 9) of green dwarf banana and characterizing their quality changes during maturation. Spectra were extracted from three zones (pedicel, middle and apex zone) of each banana finger, respectively. Based on spectra of each zone, maturity identification models with high accuracy (all over 91.53% in validation set) were established by partial least squares discrimination analysis (PLSDA) method with raw spectra. A further generic PLSDA model with an accuracy of 94.35% for validation was created by the three zones’ spectra pooled to omit the effect of spectra acquisition position. Additionally, a spectral interval was selected to simplify the generic PLSDA model, and an interval PLSDA model was built with an accuracy of 85.31% in the validation set. For characterizing some main quality parameters (soluble solid content, SSC; total acid content, TA; chlorophyll content and total chromatism, ΔE*) of banana, full-spectra partial least squares (PLS) models and interval PLS models were, respectively, developed to correlate those parameters with spectral data. In full-spectra PLS models, high coefficients of determination (R2) were 0.74 for SSC, 0.68 for TA, and fair of 0.42 as well as 0.44 for chlorophyll and ΔE*. The performance of interval PLS models was slightly inferior to that of the full-spectra PLS models. Results suggested that models for SSC and TA had an acceptable predictive ability (R2 = 0.64 and 0.59); and models for chlorophyll and ΔE* (R2 = 0.34 and 0.30) could just be used for sample screening. Visualization maps of those quality parameters were also created by applying the interval PLS models on each pixel of the hyperspectral image, the distribution of quality parameters in which were basically consistent with the actual measurement. This study proved that the hyperspectral imaging is a useful tool to assess the maturity level and quality of dwarf bananas. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

17 pages, 4541 KiB  
Article
Online Detection and Classification of Moldy Core Apples by Vis-NIR Transmittance Spectroscopy
by Kaixu Zhang, Hongzhe Jiang, Haicheng Zhang, Zequn Zhao, Yingjie Yang, Shudan Guo and Wei Wang
Agriculture 2022, 12(4), 489; https://doi.org/10.3390/agriculture12040489 - 30 Mar 2022
Cited by 9 | Viewed by 2614
Abstract
Apple moldy core disease is a common internal fungal disease. The online detection and classification of apple moldy core plays a vital role in apple postharvest processing. In this paper, an online non-destructive detection system for apple moldy core disease was developed using [...] Read more.
Apple moldy core disease is a common internal fungal disease. The online detection and classification of apple moldy core plays a vital role in apple postharvest processing. In this paper, an online non-destructive detection system for apple moldy core disease was developed using near-infrared transmittance spectroscopy in spectral range of 600–1100 nm. A total of 120 apple samples were selected and randomly divided into a training set and a test set based on the ratio of 2:1. First, basic parameters for detection of apples with moldy core were determined through detection experiments of samples in a stationary state. Due to the random distribution of the diseased tissue inside diseased apples, stationary detection cannot accurately identify the diseased tissue. To solve this problem, the spectra of apples in motion state transmitted forward by the transmission line were acquired. Three placement orientations of the apple in the carrying fruit cup were tested to explore the influence of fruit orientation on spectral characteristics and prediction. According to the performance of the model, the optimal preprocessing method and modeling method were determined (fixed orientation model and arbitrary orientation model). SPA was used to select the characteristic wavelengths to further improve the online detection speed. The overall results showed that the multi-spectra model using mean spectra of three orientations was the best. The prediction accuracies of multi-spectra model using SPA for three orientations were 96.7%, 97.5% and 97.5% respectively. As a conclusion, the arbitrary orientation model was beneficial to improve the online detection of apple moldy core disease. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

15 pages, 3288 KiB  
Article
Single- and Multiple-Adulterants Determinations of Goat Milk Powder by NIR Spectroscopy Combined with Chemometric Algorithms
by Xin Zhao, Yunpeng Wang, Xin Liu, Hongzhe Jiang, Zhilei Zhao, Xiaoying Niu, Chunhua Li, Bin Pang and Yanlei Li
Agriculture 2022, 12(3), 434; https://doi.org/10.3390/agriculture12030434 - 21 Mar 2022
Cited by 2 | Viewed by 2796
Abstract
In this work, we quantified goat milk powder adulteration by adding urea, melamine, and starch individually and simultaneously, with the utilization of near infrared (NIR) spectroscopy coupled with chemometrics. For single-adulterant samples, the successive projections algorithm (SPA) selected three, three, and four optimal [...] Read more.
In this work, we quantified goat milk powder adulteration by adding urea, melamine, and starch individually and simultaneously, with the utilization of near infrared (NIR) spectroscopy coupled with chemometrics. For single-adulterant samples, the successive projections algorithm (SPA) selected three, three, and four optimal wavelengths for urea, melamine, and starch, respectively. Models were built based on partial least squares regression (PLS) and the selected wavelengths, exhibiting good predictive ability with an Rp2 above 0.987 and an RMSEP below 0.403%. For multiple-adulterants samples, PLS2 and multivariate curve resolution alternating least squares (MCR-ALS) were adopted to build the models to quantify the three adulterants simultaneously. The PLS2 results showed adequate precision and results better than those of MCR-ALS. Except for urea, MCR-ALS models presented good predictive results for milk, melamine, and starch concentrations. MCR-ALS allowed detection of adulteration with new and unknown substitutes as well as the development of models without the need for the usage of a large data set. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Graphical abstract

15 pages, 1820 KiB  
Article
Evaluating the Waterlogging Tolerance of Wheat Cultivars during the Early Growth Stage Using the Comprehensive Evaluation Value and Digital Image Analysis
by Xiaoyi Jiang, Dandong Mao, Min Zhu, Xingchun Wang, Chunyan Li, Xinkai Zhu, Wenshan Guo and Jinfeng Ding
Agriculture 2022, 12(3), 384; https://doi.org/10.3390/agriculture12030384 - 09 Mar 2022
Cited by 2 | Viewed by 2056
Abstract
The accurate and efficient screening of waterlogging-tolerant cultivars is an effective way to mitigate waterlogging damages. An experiment was conducted to evaluate the performance of 28 wheat varieties mainly planted in the middle and lower reaches of the Yangtze River, China, under control [...] Read more.
The accurate and efficient screening of waterlogging-tolerant cultivars is an effective way to mitigate waterlogging damages. An experiment was conducted to evaluate the performance of 28 wheat varieties mainly planted in the middle and lower reaches of the Yangtze River, China, under control and waterlogging conditions. When the 15-day waterlogging that was initiated at the third-leaf stage was completed, the aboveground dry weight, plant height, leaf number on main stem, culm number, leaf area, and SPAD readings of wheat seedlings were significantly decreased by 14%, 11%, 6%, 13%, 14%, and 15% compared with the control treatment (maintaining approximately 80% of field capacity), respectively. The results showed that the percentage reductions in the dry weight and leaf area under stress accurately represented the influence of the majority of the measured agronomic traits and were significantly negatively correlated with the respective dry weight and leaf area of different cultivars under waterlogging. This suggests that dry weight and leaf area can be used as agronomic traits for screening waterlogging-tolerant cultivars. The comprehensive evaluation value of waterlogging tolerance (CEVW) was closely related to the percentage reduction in dry weight, plant height, culm number, leaf area, and SPAD reading. The range of CEVW was 0.187–0.819, indicating a wide variation in the waterlogging tolerance of the wheat cultivars. Comparing the top-view images, the phenotypic texture parameters (dissimilarity, homogeneity, and angular second moment (ASM)) extracted from the side-view images better reflected the dry weight, plant height, and leaf area under different water treatments. The percentage reduction in ASM had the strongest correlation with CEVW (root mean square error = 0.109); thus, the ASM is recommended as a suitable phenotypic parameter to evaluate waterlogging tolerance. The present results provide references for the rapid and intelligent screening of waterlogging-tolerant wheat cultivars, but future studies need to consider the stress evaluation of the adult plants. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

16 pages, 3819 KiB  
Article
Measuring Method of Slip Ratio for Tractor Driving Wheels Based on Machine Vision
by Shaohua Zhu, Lin Wang, Zhongxiang Zhu, Enrong Mao, Yiming Chen, Yuxi Liu and Xianxu Du
Agriculture 2022, 12(2), 292; https://doi.org/10.3390/agriculture12020292 - 17 Feb 2022
Cited by 5 | Viewed by 3357
Abstract
Tractors are prone to large slips when they are in field operation. The degree of slip plays a vital role in traction efficiency and fuel efficiency. This paper presents a method for measuring the slip ratio of tractors in field operation based on [...] Read more.
Tractors are prone to large slips when they are in field operation. The degree of slip plays a vital role in traction efficiency and fuel efficiency. This paper presents a method for measuring the slip ratio of tractors in field operation based on machine vision. The accurate measurement of slip ratio needs to obtain actual velocity and theoretical velocity separately. For obtaining the actual velocity, a monocular camera mounted on the tractor vertically faces down at the ground to collect images. Then, the feature points of inter-frame ground images are matched by the ORB (Oriented FAST and Rotated BRIEF) algorithm for calculating the translational displacement. Next, a homography matrix based on camera calibration is proposed to complete the transformation of a point from the pixel coordinate system to the world coordinate system. Aiming to acquire the theoretical velocity, a method that takes the variations in tire radius into account is proposed, and the tire radii of the driving wheels are indirectly determined by the tire inflation pressure in real-time. The proposed measurement method was verified with an experimental tractor. The results show that the mean absolute errors of the tractor driving wheels’ slip ratio measured by the machine vision method are less than 0.75%, and the maximum of the absolute errors is not more than 2.22%, which shows good performance. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

11 pages, 2982 KiB  
Article
A High Sensitivity Electrochemical Immunosensor Based on Monoclonal Antibody Coupled Flower-Shaped Nano-ZnO for Detection of Tenuazonic Acid
by Chi Zhang, Congcong Du, Wei Liu, Ting Guo, Ying Zhou, Hongyuan Zhou, Yuhao Zhang, Xiaozhu Liu and Liang Ma
Agriculture 2022, 12(2), 204; https://doi.org/10.3390/agriculture12020204 - 01 Feb 2022
Cited by 1 | Viewed by 2036
Abstract
In this paper, an electrochemical biosensor was established for the high-sensitivity detection of Tenuazonic acid (TeA) in fruits based on the enrichment of flower-shaped nano-ZnO and the specific recognition of immune response. Herein flower-shaped nano-ZnO (ZnO NFs) with a hexagonal wurtzite structure and [...] Read more.
In this paper, an electrochemical biosensor was established for the high-sensitivity detection of Tenuazonic acid (TeA) in fruits based on the enrichment of flower-shaped nano-ZnO and the specific recognition of immune response. Herein flower-shaped nano-ZnO (ZnO NFs) with a hexagonal wurtzite structure and diameter of 700–800 nm were demonstrated to have the optimal specific surface area and outstanding conductivity, compared with different morphology, sizes, and crystal structures of nano-ZnO. Second, the ZnO NFs were used as carriers for efficiently immobilizing monoclonal antibodies to obtain antibody bioconjugates, which were anchored on the 2-mercaptobenzoic acid-modified gold electrode by amide reaction. In the presence of TeA, the monoclonal antibody could specifically recognize and bind to it, resulting in a decrease in electron transfer ability on the gold electrode surface. Finally, the electrochemical biosensor showed a range from 5 × 10−5 μg/mL to 5 × 10−1 μg/mL with a detection limit of 1.14 × 10−5 μg/mL. Furthermore, it exhibited high selectivity for TeA among other analogs, such as Altenuene (ALT) and Alternariol (AOH). Notably, the proposed strategy could be employed to monitor TeA in tomato and citrus, showing potential application prospects in practical application and commercial value. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

16 pages, 4770 KiB  
Article
Application of Fourier Transform Infrared Spectroscopy and Multivariate Analysis Methods for the Non-Destructive Evaluation of Phenolics Compounds in Moringa Powder
by Rahul Joshi, Ramaraj Sathasivam, Sang Un Park, Hongseok Lee, Moon S. Kim, Insuck Baek and Byoung-Kwan Cho
Agriculture 2022, 12(1), 10; https://doi.org/10.3390/agriculture12010010 - 22 Dec 2021
Cited by 10 | Viewed by 3430
Abstract
This study performed non-destructive measurements of phenolic compounds in moringa powder using Fourier Transform Infrared (FT-IR) spectroscopy within a spectral range of 3500–700 cm−1. Three major phenolic compounds, namely, kaempferol, benzoic acid, and rutin, were measured in five different varieties of [...] Read more.
This study performed non-destructive measurements of phenolic compounds in moringa powder using Fourier Transform Infrared (FT-IR) spectroscopy within a spectral range of 3500–700 cm−1. Three major phenolic compounds, namely, kaempferol, benzoic acid, and rutin, were measured in five different varieties of moringa powder, which was approved with respect to the high-performance liquid chromatography (HPLC) method. The prediction performance of three different regression methods, i.e., partial least squares regression (PLSR), principal component regression (PCR), and net analyte signal (NAS)-based methodology, called hybrid linear analysis (HLA/GO), were compared to achieve the best prediction model. The obtained results for the PLS regression method resulted in better performance for the prediction analysis of phenolic compounds in moringa powder. The PLSR model attained a correlation coefficient (Rp2) value of 0.997 and root mean square error of prediction (RMSEP) of 0.035 mg/g, respectively, which is comparatively higher than the other two regression models. Based on the results, it can be concluded that FT-IR spectroscopy in conjugation with a suitable regression analysis method could be an effective analytical tool for the non-destructive prediction of phenolic compounds in moringa powder. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

19 pages, 15163 KiB  
Article
Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm
by Xingpeng Li, Hongzhe Jiang, Xuesong Jiang and Minghong Shi
Agriculture 2021, 11(12), 1274; https://doi.org/10.3390/agriculture11121274 - 15 Dec 2021
Cited by 28 | Viewed by 3157
Abstract
The adulteration in Chinese chestnuts affects the quality, taste, and brand value. The objective of this study was to explore the feasibility of the hyperspectral imaging (HSI) technique to determine the geographical origin of Chinese chestnuts. An HSI system in spectral range of [...] Read more.
The adulteration in Chinese chestnuts affects the quality, taste, and brand value. The objective of this study was to explore the feasibility of the hyperspectral imaging (HSI) technique to determine the geographical origin of Chinese chestnuts. An HSI system in spectral range of 400–1000 nm was applied to identify a total of 417 Chinese chestnuts from three different geographical origins. Principal component analysis (PCA) was preliminarily used to investigate the differences of average spectra of the samples from different geographical origins. A deep-learning-based model (1D-CNN, one-dimensional convolutional neural network) was developed first, and then the model based on full spectra and optimal wavelengths were established for various machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The optimal results based on full spectra for 1D-CNN, PLS-DA, and PSO-SVM models were 97.12%, 97.12%, and 95.68%, respectively. Competitive adaptive reweighted sampling (CARS) and a successive projections algorithm (SPA) were individually utilized for wavelengths selection, and the results of simplified models generally improved. The contrasting results demonstrated that the prediction accuracies of SPA-PLS-DA and 1D-CNN both reached 97.12%, but 1D-CNN presented a higher Kappa coefficient value than SPA-PLS-DA. Meanwhile, the sensitivities and specificities of SPA-PLS-DA and 1D-CNN models were both above 90% for the samples from each geographical origin. These results indicated that both SPA-PLS-DA and 1D-CNN models combined with HSI have great potential for the geographical origin identification of Chinese chestnuts. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

17 pages, 6820 KiB  
Article
Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm
by Xiaoyu Li, Yuefeng Du, Lin Yao, Jun Wu and Lei Liu
Agriculture 2021, 11(12), 1238; https://doi.org/10.3390/agriculture11121238 - 08 Dec 2021
Cited by 14 | Viewed by 2828
Abstract
At present, the wide application of the CNN (convolutional neural network) algorithm has greatly improved the intelligence level of agricultural machinery. Accurate and real-time detection for outdoor conditions is necessary for realizing intelligence and automation of corn harvesting. In view of the problems [...] Read more.
At present, the wide application of the CNN (convolutional neural network) algorithm has greatly improved the intelligence level of agricultural machinery. Accurate and real-time detection for outdoor conditions is necessary for realizing intelligence and automation of corn harvesting. In view of the problems with existing detection methods for judging the integrity of corn kernels, such as low accuracy, poor reliability, and difficulty in adapting to the complicated and changeable harvesting environment, this paper investigates a broken corn kernel detection device for combine harvesters by using the yolov4-tiny model. Hardware construction is first designed to acquire continuous images and processing of corn kernels without overlap. Based on the images collected, the yolov4-tiny model is then utilized for training recognition of the intact and broken corn kernels samples. Next, a broken corn kernel detection algorithm is developed. Finally, the experiments are carried out to verify the effectiveness of the broken corn kernel detection device. The laboratory results show that the accuracy of the yolov4-tiny model is 93.5% for intact kernels and 93.0% for broken kernels, and the value of precision, recall, and F1 score are 92.8%, 93.5%, and 93.11%, respectively. The field experiment results show that the broken kernel rate obtained by the designed detection device are in good agreement with that obtained by the manually calculated statistic, with differentials at only 0.8%. This study provides a technical reference of a real-time method for detecting a broken corn kernel rate. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

21 pages, 2605 KiB  
Article
A High-Performance Database Management System for Managing and Analyzing Large-Scale SNP Data in Plant Genotyping and Breeding Applications
by Yikun Zhao, Bin Jiang, Yongxue Huo, Hongmei Yi, Hongli Tian, Haotian Wu, Rui Wang, Jiuran Zhao and Fengge Wang
Agriculture 2021, 11(11), 1027; https://doi.org/10.3390/agriculture11111027 - 20 Oct 2021
Cited by 2 | Viewed by 2326
Abstract
A DNA fingerprint database is an efficient, stable, and automated tool for plant molecular research that can provide comprehensive technical support for multiple fields of study, such as pan-genome analysis and crop breeding. However, constructing a DNA fingerprint database for plants requires significant [...] Read more.
A DNA fingerprint database is an efficient, stable, and automated tool for plant molecular research that can provide comprehensive technical support for multiple fields of study, such as pan-genome analysis and crop breeding. However, constructing a DNA fingerprint database for plants requires significant resources for data output, storage, analysis, and quality control. Large amounts of heterogeneous data must be processed efficiently and accurately. Thus, we developed plant SNP database management system (PSNPdms) using an open-source web server and free software that is compatible with single nucleotide polymorphism (SNP), insertion–deletion (InDel) markers, Kompetitive Allele Specific PCR (KASP), SNP array platforms, and 23 species. It fully integrates with the KASP platform and allows for graphical presentation and modification of KASP data. The system has a simple, efficient, and versatile laboratory personnel management structure that adapts to complex and changing experimental needs with a simple workflow process. PSNPdms internally provides effective support for data quality control through multiple dimensions, such as the standardized experimental design, standard reference samples, fingerprint statistical selection algorithm, and raw data correlation queries. In addition, we developed a fingerprint-merging algorithm to solve the problem of merging fingerprints of mixed samples and single samples in plant detection, providing unique standard fingerprints of each plant species for construction of a standard DNA fingerprint database. Different laboratories can use the system to generate fingerprint packages for data interaction and sharing. In addition, we integrated genetic analysis into the system to enable drawing and downloading of dendrograms. PSNPdms has been widely used by 23 institutions and has proven to be a stable and effective system for sharing data and performing genetic analysis. Interested researchers are required to adapt and further develop the system. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Graphical abstract

Review

Jump to: Research

22 pages, 6580 KiB  
Review
A Review of Methods and Techniques for Detecting Frost on Plant Surfaces
by Huan Song, Yongguang Hu, Yongzong Lu, Jizhang Wang, Qingmin Pan and Pingping Li
Agriculture 2021, 11(11), 1142; https://doi.org/10.3390/agriculture11111142 - 15 Nov 2021
Cited by 2 | Viewed by 4457
Abstract
Severe frost usually has adverse impacts on agricultural production, resulting in crop freeze injury, poor crop yield, and crop quality reduction. Timely and accurate detection of frost plays an important role in cold damage warnings, prevention, and control. Current frost detection methods mostly [...] Read more.
Severe frost usually has adverse impacts on agricultural production, resulting in crop freeze injury, poor crop yield, and crop quality reduction. Timely and accurate detection of frost plays an important role in cold damage warnings, prevention, and control. Current frost detection methods mostly use physical properties such as light, electricity, and heat, or the judge and quantify using environmental factors such as temperature and wind speed. However, it is difficult to detect and accurately identify the frosting phenomenon in real time during field trials because of the complex environment, different plant types, and interference by many factors during observation. To provide an overview of the analytical tools for scientists, researchers, and product developers, a review and comparative analysis of the available literature on frost mechanisms, correlations, and characteristics are presented in this study. First, the mechanisms of the frost formation process, frost level, and the significance of detection, are introduced. Then, the methods and techniques used to measure frost on plant surfaces are synthetically classified and further compared. Moreover, the key points and difficulties are summarized and discussed. Finally, some constructive methods of frost detection are proposed to improve the frost detection process. Full article
(This article belongs to the Special Issue Sensors Applied to Agricultural Products)
Show Figures

Figure 1

Back to TopTop