Submit to Agronomy Review for Agronomy Propose a Special Issue

Journal Browser

Frontier Studies in Crop Growth Monitoring, Diagnosis and Precision Operation

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Agricultural Biosystem and Biological Engineering".

Deadline for manuscript submissions: closed (25 July 2022) | Viewed by 16684

Share This Special Issue

Special Issue Editors

Prof. Dr. Jun Ni

E-Mail Website
Guest Editor

College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: agricultural smart sensor; agricultural intelligent equipment
Special Issues, Collections and Topics in MDPI journals

Dr. Lei Feng

E-Mail Website
Guest Editor

College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Interests: agricultural systems engineering; agricultural electrification and automation
Special Issues, Collections and Topics in MDPI journals

Dr. Zhenbin Hu

E-Mail Website
Guest Editor

Department of Biology, Saint Louis University, St. Louis, MO, USA
Interests: crop growth monitoring; crop physiology and ecology; high-throughput phenotyping

Special Issue Information

Dear Colleagues,

The basic requirement of the precision management and regulation of crops is to rapidly acquire accurate and reliable growth information in a convenient way at a low cost, also being the key to realizing precise fertilization. For a long time, crop growth information was acquired through destructive sampling in the field and indoor biochemical measurements. Although the results are reliable, these methods are laborious, take a long time and exhibit an undesirable timeliness not suitable for the implementation of site-specific fertilization.

Therefore, it is necessary to adopt modern technologies to real-time monitor and diagnose crop growth, obtain and analyze crop fertilizer and water regulation information online with equipment and methods, and utilize various kinds of sensors, devices and platforms to complete the precision operation of crops.

This Special Issue welcomes papers involved in research on crop growth monitoring, diagnosis and precision operations showing how to integrate a monitoring method, sensor technology and operation equipment used in crop production management. Specific topics include, but are not limited to, frontier studies in crop growth monitoring, diagnosis and precision operations, as well as:

Innovative methods regarding the nondestructive monitoring and diagnosis of crop growth;
Intelligent analysis and extraction of crop growth features based on spectra, images, etc.;
Real-time monitoring and detection sensors for crop growth;
Variable fertilization or irrigation technology and operation equipment for crops;
Advances, innovations and new trends in modern technologies for crop growth monitoring, diagnosis and precision operation.

Prof. Dr. Jun Ni
Dr. Lei Feng
Dr. Zhenbin Hu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

crop growth monitoring
crop growth diagnosis
sensor
operation equipment
precision agriculture
artificial intelligence

Published Papers (9 papers)

Download All Papers

Research

Jump to: Other

27 pages, 11966 KiB

Open AccessArticle

Design of the Mechanical Structure of a Field-Based Crop Phenotyping Platform and Tests of the Platform

by Huali Yuan, Yiming Liu, Minghan Song, Yan Zhu, Weixing Cao, Xiaoping Jiang and Jun Ni

Agronomy 2022, 12(9), 2162; https://doi.org/10.3390/agronomy12092162 - 11 Sep 2022

Cited by 4 | Viewed by 1704

Abstract

The field mobile platform is an important tool for high-throughput phenotype monitoring. To overcome problems in existing field-based crop phenotyping platforms, including limited application scope and low stability, a rolling adjustment method for the wheel tread was proposed. A self-propelled three-wheeled field-based crop phenotyping platform with variable wheel tread and height above ground was developed, which enabled phenotypic information of different dry crops in different development stages. A three-dimensional model of the platform was established using Pro/E; ANSYS and ADAMS were used for static and dynamic performance. Results show that when running on flat ground, the platform has a vibration acceleration lower than 0.5 m/s². When climbing over an obstacle with a height of 100 mm, the vibration amplitude of the platform is 88.7 mm. The climbing angle is not less than 15°. Field tests imply that the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) of a canopy measured using crop growth sensors mounted on the above platform show favorable linear correlations with those measured using a handheld analytical spectral device (ASD). Their R² values are 0.6052 and 0.6093 and root-mean-square errors (RMSEs) are 0.0487 and 0.1521, respectively. The field-based crop phenotyping platform provides a carrier for high-throughput acquisition of crop phenotypic information. Full article

(This article belongs to the Special Issue Frontier Studies in Crop Growth Monitoring, Diagnosis and Precision Operation)

► Show Figures

Figure 1

25 pages, 6872 KiB

Open AccessArticle

Development of a Crop Spectral Reflectance Sensor

by Naisen Liu, Wenyu Zhang, Fuxia Liu, Meina Zhang, Chenggong Du, Chuanliang Sun, Jing Cao, Shuwen Ji and Hui Sun

Agronomy 2022, 12(9), 2139; https://doi.org/10.3390/agronomy12092139 - 08 Sep 2022

Cited by 1 | Viewed by 1558

Abstract

In this study, a low-cost, self-balancing crop spectral reflectance sensor (CSRS) was designed for real-time, nondestructive monitoring of the spectral reflectance and vegetation index of crops such as tomato and rapeseed. The sensor had a field of view of 30°, and a narrow-band filter was used for light splitting. The filter’s full width at half-maximum was 10 nm, and the spectral bands were 710 nm and 870 nm. The sensor was powered by a battery and used WiFi for communication. Its software was based on the Contiki operating system. To make the sensor work in different light intensity conditions, the photoelectric conversion automatic gain circuit had a total of 255 combinations of amplification. The gimbal of the sensor was mainly composed of an inner ring and an outer ring. Under the gravity of the sensor, the central axis of the sensor remained vertical, such that the up-facing and down-facing photosensitive units stayed in the horizontal position. The mechanical components of the sensor were designed symmetrically to facilitate equal mass distribution and to meet the needs of automatic balancing. Based on the optical signal transmission process of the sensor and the dark-current characteristics of the photodetector, a calibration method was theoretically deduced, which improved the accuracy and stability of the sensor under different ambient light intensities. The calibration method is also applicable for the calibration of other crop growth information sensors. Next, the standard reflectance gray scale was taken as the measurement variable to test the accuracy of the sensor, and the results showed that the root mean square error of the reflectance measured by the sensor at 710 nm and 870 nm was 1.10% and 1.27%, respectively; the mean absolute error was 0.95% and 0.89%, respectively; the relative error was below 4% and 3%, respectively; and the coefficient of variation was between 1.0% and 2.5%. The reflectance data measured by the sensor under different ambient light intensities suggested that the absolute error of the sensor was within ±0.5%, and the coefficients of variation at the two spectral bands were 1.04% and 0.39%, respectively. With tomato and rapeseed as the monitoring targets, the proposed CSRS and a commercial spectroradiometer were used to measure at the same time. The results showed that the reflectance measured by the two devices was very close, and there was a linear relationship between the normalized difference vegetation index of the CSRS and that of the commercial spectroradiometer. The coefficient of determination (R²) for tomato and rapeseed were 0.9540 and 0.9110, respectively. Full article

(This article belongs to the Special Issue Frontier Studies in Crop Growth Monitoring, Diagnosis and Precision Operation)

► Show Figures

Figure 1

16 pages, 3710 KiB

Open AccessArticle

DS-DETR: A Model for Tomato Leaf Disease Segmentation and Damage Evaluation

by Jianshuang Wu, Changji Wen, Hongrui Chen, Zhenyu Ma, Tian Zhang, Hengqiang Su and Ce Yang

Agronomy 2022, 12(9), 2023; https://doi.org/10.3390/agronomy12092023 - 26 Aug 2022

Cited by 7 | Viewed by 2263

Abstract

Early blight and late blight are important factors restricting tomato yield. However, it is still a challenge to accurately and objectively detect and segment crop diseases in order to evaluate disease damage. In this paper, the Disease Segmentation Detection Transformer (DS-DETR) is proposed to segment leaf disease spots efficiently based on several improvements to DETR. Additionally, a damage assessment is carried out by the area ratio of the segmented leaves to the disease spots. First, an unsupervised pre-training method was introduced into DETR with the Plant Disease Classification Dataset (PDCD) to solve the problem of the long training epochs and slow convergence speed of DETR. This method can train the Transformer structures in advance to obtain leaf disease features. Loading the pre-training model weight in DS-DETR can speed up the convergence speed of the model. Then, Spatially Modulated Co-Attention (SMCA) was used to assign Gaussian-like spatial weights to the query box of DS-DETR. The different positions in the image are trained using the query boxes with different weights to improve the accuracy of the model. Finally, an improved relative position code was added to the Transformer structure of DS-DETR. Relative position coding promotes the capture of the sequence order of input tokens by the Transformer. The spatial location feature is strengthened by establishing the location relationship between different instances. Based on these improvements, the DS-DETR model was tested on the Tomato leaf Disease Segmentation Dataset (TDSD) constructed by us. The experimental results show that the DS-DETR proposed by us achieved 0.6823 for AP_mask, which improved by 12.87%, 8.25%, 3.67%, 1.95%, 10.27%, and 9.52% compared with the state-of-the-art: Mask RCNN, BlendMask, CondInst, SOLOv2, ISTR, and DETR, respectively. In addition, the disease grading accuracy reached 0.9640 according to the segmentation results given by our proposed model. Full article

(This article belongs to the Special Issue Frontier Studies in Crop Growth Monitoring, Diagnosis and Precision Operation)

► Show Figures

Figure 1

15 pages, 13765 KiB

Open AccessArticle

Estimating Leaf Nitrogen Content in Wheat Using Multimodal Features Extracted from Canopy Spectra

by Zhiwei Gao, Na Luo, Baohua Yang and Yue Zhu

Agronomy 2022, 12(8), 1915; https://doi.org/10.3390/agronomy12081915 - 14 Aug 2022

Cited by 3 | Viewed by 1470

Abstract

The leaf nitrogen content (LNC) of wheat is one of key bases for wheat nitrogen fertilizer management and nutritional diagnosis, which is of great significance to the sustainable development of precision agriculture. The canopy spectrum provides an effective way to monitor the nitrogen content of wheat. Previous studies have shown that features extracted from the canopy spectrum, such as vegetation indices (VIs) and band positions (BPs), have successfully achieved the monitoring of crop nitrogen nutrition. However, the features mentioned above are spectral features extracted on the basis of linear or nonlinear combination models with a simple structure, which limits the general applicability of the model. In addition, models based on spectral features are prone to overfitting, which also reduces the accuracy of the model. Therefore, we propose an estimation model based on multimodal features (convolutional features and VIs, BPs) of the canopy spectrum, which aim to improve accuracy in estimating wheat LNC. Among these, the convolutional features (CFs) extracted by the designed convolutional neural network represent the deep semantic information of the canopy reflection spectrum, which can make up for the lack of robustness of the spectral features. The results showed that the accuracy of the model based on the fusion features (VIs + BPs + CFs) was higher than that of the feature of single modality. Moreover, the particle swarm optimization–support vector regression (PSO-SVR) model based on multimodal features had the best prediction effect (R² = 0.896, RMSE = 0.188 for calibration, R² = 0.793, RMSE = 0.408 for validation). Therefore, the method proposed in this study could improve performance in the estimation of wheat LNC, which provides technical support for wheat nitrogen nutrition monitoring. Full article

(This article belongs to the Special Issue Frontier Studies in Crop Growth Monitoring, Diagnosis and Precision Operation)

► Show Figures

Figure 1

22 pages, 3352 KiB

Open AccessArticle

Estimation of Nitrogen Content Based on the Hyperspectral Vegetation Indexes of Interannual and Multi-Temporal in Cotton

by Lulu Ma, Xiangyu Chen, Qiang Zhang, Jiao Lin, Caixia Yin, Yiru Ma, Qiushuang Yao, Lei Feng, Ze Zhang and Xin Lv

Agronomy 2022, 12(6), 1319; https://doi.org/10.3390/agronomy12061319 - 30 May 2022

Cited by 10 | Viewed by 1621

Abstract

Crop nitrogen is an efficient index for estimating crop yield. Using hyperspectral information to monitor nitrogen in cotton information in real time can help guide cotton cultivation. In this study, we used drip-irrigation cotton in Xinjiang as the research object and employed various nitrogen treatments to explore the correlation between hyperspectral vegetation indexes and leaf nitrogen concentration (LNC) and the canopy nitrogen density (CND) of cotton in different growth periods and interannual. We employed 30 published hyperspectral vegetation indexes obtained through spectral monitoring in 2019 and 2020 to screen for hyperspectral vegetation indexes highly correlated with the nitrogen in cotton indexes. Based on the same group of hyperspectral vegetation indexes, interannual and multi-temporal nitrogen estimation models of cotton were established using three modeling methods: simple multiple linear regression (MLR), partial least-squares regression (PLSR), and support vector regression (SVR). The results showed the following: (1) The correlations between LNC and CND and vegetation index in individual growth periods of cotton were lower than those for the entire growth period. The correlations between hyperspectral vegetation indexes and cotton LNC, CND, leaf area index (LAI), and aboveground biomass (AGB), were significantly different between years and varieties. The relatively stable indexes between vegetation and LNC were TCARI, PRI, CCRI, and SRI-2, and the absolute values of correlation were 0.251~0.387, 0.239~0.422, 0.245~0.387, and 0.357~0.533. In addition, the correlation between CIred-edge and REIlinear and group indicators (CND, AGB, and LAI) was more stable. (2) In the models established by MLR, PLSR, and SVR, the R² value from the SVR method was higher in the estimation model based on the entire growth period data and LNC and CND. (3) Using the same group of selected hyperspectral vegetation indexes to estimate nitrogen in cotton in different growth stages, the accuracy of the estimation model of canopy nitrogen density (CND) was higher than that of the estimation model for leaf nitrogen concentration. The canopy nitrogen density most stable model was established by MLR at the flowering and boll stages and the full-boll stage with R² = 0.532~0.665. This study explored the application potential of hyperspectral vegetation indexes to the nitrogen of drip-irrigated cotton, and the results provide a theoretical basis for hyperspectral monitoring for crop nutrients and canopy structure. Full article

(This article belongs to the Special Issue Frontier Studies in Crop Growth Monitoring, Diagnosis and Precision Operation)

► Show Figures

Figure 1

11 pages, 6748 KiB

Open AccessArticle

Design and Test of Cleaning Loss Kernel Recognition System for Corn Combine Harvester

by Min Zhang, Lan Jiang, Chongyou Wu and Gang Wang

Agronomy 2022, 12(5), 1145; https://doi.org/10.3390/agronomy12051145 - 09 May 2022

Cited by 4 | Viewed by 1382

Abstract

Cleaning loss is an important parameter to judge the performance of a corn combine harvester. At present, there exists the phenomenon that corn cleaning loss rate detection devices have a long signal processing time and low recognition accuracy. To solve this problem, based on the principle of the impacting piezoelectric effect, the impacting signals of corn kernels and impurities are analyzed by means of the frequency spectrum method to obtain the characteristic frequency for effectively distinguishing corn kernels and impurities, which is determined as 8.7 kHz. Based on this characteristic frequency, a corn cleaning loss kernel recognition system is designed, which can realize the function of corn kernel recognition and cleaning loss rate recording. In this system, signal processing circuits which mainly include two-order high-pass filtration, envelope wave detection and voltage comparison are designed. On the basis of the signal processing circuit, adding the judgment program for the holding time of the output square wave signal improves the system’s recognition accuracy for kernels impacting the sensitive plate simultaneously. The system was tested in indoor conditions. The results show that 20–30 corn kernels could be accurately recognized per minute on a single sensitive plate, and the recognition accuracy rate of this system could reach 85% when three corn kernels impacted simultaneously. The results serve as a theoretical basis and represent a new method for the design of a cleaning loss kernel recognition system. Full article

(This article belongs to the Special Issue Frontier Studies in Crop Growth Monitoring, Diagnosis and Precision Operation)

► Show Figures

Figure 1

10 pages, 20055 KiB

Open AccessArticle

Describing Lettuce Growth Using Morphological Features Combined with Nonlinear Models

by Qinglin Li, Hongyan Gao, Xiaodong Zhang, Jiheng Ni and Hanping Mao

Agronomy 2022, 12(4), 860; https://doi.org/10.3390/agronomy12040860 - 31 Mar 2022

Cited by 5 | Viewed by 2464

Abstract

The aim of this study was to describe the sigmoidal growth behaviour of a lettuce canopy using three nonlinear models. Gompertz, Logistic and grey Verhulst growth models were established for the top projected canopy area (TPCA), top projected canopy perimeter (TPCP) and plant height (PH), which were measured by two machine vision views and 3D point clouds data. Satisfactory growth curve fitting was obtained using two evaluation criteria: the coefficient of determination (R²) and the mean absolute percentage error (MAPE). The grey Verhulst models produced a better fit for the growth of TPCA and TPCP, with higher R² (

R_{T P C A}^{2} = 0.9097, R_{T P C P}^{2} = 0.8536

) and lower MAPE (

M A P E_{T P C A}^{} = 0.0284, M A P E_{T P C P}^{} = 0.0794

) values, whereas the Logistic model produced a better fit for changes in PH (

R_{P H}^{2} = 0.8991, M A P E_{P H}^{} = 0.0344

). The maximum growth rate point and the beginning and end points of the rapid growth stage were determined by calculating the second and third derivatives of the models, permitting a more detailed description of their sigmoidal behaviour. The initial growth stage was 1–5.5 days, and the rapid growth stage lasted from 5.6 to 26.2 days. After 26.3 days, lettuce entered the senescent stage. These inflections and critical points can be used to gain a better understanding of the growth behaviour of lettuce, thereby helping researchers or agricultural extension agents to promote growth, determine the optimal harvest period and plan commercial production. Full article

(This article belongs to the Special Issue Frontier Studies in Crop Growth Monitoring, Diagnosis and Precision Operation)

► Show Figures

Figure 1

20 pages, 42490 KiB

Open AccessArticle

Monitoring of Nitrogen Indices in Wheat Leaves Based on the Integration of Spectral and Canopy Structure Information

by Huaimin Li, Donghang Li, Ke Xu, Weixing Cao, Xiaoping Jiang and Jun Ni

Agronomy 2022, 12(4), 833; https://doi.org/10.3390/agronomy12040833 - 29 Mar 2022

Cited by 6 | Viewed by 1924

Abstract

Canopy spectral reflectance can indicate both crop nutrient and canopy structural information. Differences in canopy structure can affect spectral reflectance. However, a non-imaging spectrometer cannot distinguish such differences while monitoring crop nutrients, because the results are likely to be influenced by the canopy structure. In addition, nitrogen application rate is one of the main factors influencing the canopy structure of crops. Strong correlations exist between indices of canopy structure and leaf nitrogen, and thus, these can be used to compensate for the spectral monitoring of nitrogen content in wheat leaves. In this study, canopy structural indices (CSI) such as wheat coverage, height, and textural features were obtained based on the RGB and height images obtained by the RGB-D camera. Moreover, canopy spectral reflectance was obtained by an ASD hyperspectral spectrometer, based on which two vegetation indices—ratio vegetation index (RVI) and angular insensitivity vegetation index (AIVI)—were constructed. With the vegetation indices and CSIs as input parameters, a model was established to predict the leaf nitrogen content (LNC) and leaf nitrogen accumulation (LNA) of wheat based on partial least squares (PLS) and random forest (RF) regression algorithms. The results showed that the RF model with RVI and CSI as inputs had the highest prediction accuracy for LNA, the coefficient of determination (R²) reached 0.79, and the root mean square error (RMSE) was 1.54 g/m². The vegetation indices and coverage were relatively important features in the model. In addition, the PLS model with AIVI and CSI as input parameters had the highest prediction accuracy for LNC, with an R² of 0.78 and an RMSE of 0.35%, among the vegetation indices. In addition, parts of both the textural and height features were important. The results suggested that PLS and RF regression algorithms can effectively integrate spectral and canopy structural information, and canopy structural information effectively supplement spectral information by improving the prediction accuracy of vegetation indices for LNA and LNC. Full article

(This article belongs to the Special Issue Frontier Studies in Crop Growth Monitoring, Diagnosis and Precision Operation)

► Show Figures

Figure 1

Other

Jump to: Research

13 pages, 3817 KiB

Open AccessCase Report

Sundry Bacteria Contamination Identification of Lentinula Edodes Logs Based on Deep Learning Model

by Dawei Zu, Feng Zhang, Qiulan Wu, Cuihong Lu, Weiqiang Wang and Xuefei Chen

Agronomy 2022, 12(9), 2121; https://doi.org/10.3390/agronomy12092121 - 07 Sep 2022

Cited by 4 | Viewed by 1416

Abstract

Lentinula edodes logs are susceptible to sundry bacteria contamination during the culture process, and the manual identification of contaminated logs is difficult, untimely, and inaccurate. Aiming to solve this problem, this paper proposes a method for the identification of contaminated Lentinula edodes logs based on the deep learning model Ghost–YOLOv4. Firstly, a data set of Lentinula edodes log sundry bacteria contamination was constructed. Secondly, in view of the problems that the YOLOv4 network parameters are too large and that the detection speeds of Lentinula edodes log videos are slow, the backbone feature extraction network was replaced with a lightweight network, GhostNet, and the YOLOv4 enhancement feature extraction network PANet and the Yolo Head modules were completed. The modification of the network reduced the number of parameters of the network and improved the detection speed of the network. Finally, the feature extraction network introduced the migration learning pre-training model, which reduced the computational pressure and overfitting problems of the model and further improved the performance of the Ghost–YOLOv4 network. Not only did the constructed Ghost–YOLOv4 ensure the accuracy of the identification and detection of Lentinula edodes log sundry bacteria contamination, but it also had better results in detection speed and real-time performance, and it provides an effective solution for the lightweight deployment of a target detection model on embedded equipment in culture sheds. Full article

(This article belongs to the Special Issue Frontier Studies in Crop Growth Monitoring, Diagnosis and Precision Operation)

► Show Figures