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Advanced Sensors in Agriculture

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 118995

Special Issue Editor

Prof. Dr. Spyridon Kintzios

E-Mail Website
Guest Editor
Laboratory of Cell Technology, Faculty of Biotechnology, Agricultural University of Athens, Athens, Greece
Interests: biosensors; biotechnology; cell culture; cell technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensing systems are important in the development of crop and livestock production, which leads to a more sustainable intensification of agricultural systems. In particular, new sensors with reduced dimensions, reduced costs, and increased performances are available, which can be implemented and integrated in production systems, allowing an increase of data streams to be used in decision-making systems, especially in the context of precision agriculture. These advanced sensor technologies include: non-destructive sensing; proximal, aerial, and remote sensing; digital multispectral and hyperspectral sensors; fluorescence and thermal imaging sensors and navigation sensors; yield-monitoring sensors; animal-implantable biosensors; food chain production online and inline biosensors; blockchain applications in agrifood science and business. Tentative areas of sensing systems in primary agriculture could deal with, but be not limited to, the following areas:

- Soil, vegetation, air, and water sensors
- Animal behavior tracking sensors/farm management sensing systems
- Control of greenhouse and livestock buildings sensors
- Automation and control systems in agricultural machines
- Yield-monitoring sensors
- Sensing systems for the early detection of diseases and pests
- Detection and identification of crops and weeds
- Sensors for detection of fruits and quality determination
- Sensor networks and IoT in agriculture and livestock sectors
- Sensors for online/inline monitoring of food chain production and distribution from farm to fork
- Artificial intelligence and blockchain applications in agriculture
- Point-of-Test (PoT) systems in agriculture

This special issue coincides with the commemoration of the 100 years of the Agricultural University of Athens.

Prof. Dr. Spyridon Kintzios
Guest Editor

Manuscript Submission Information

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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

  • aerial sensing
  • artificial intelligence
  • automation
  • biosensor
  • blockchain
  • control systems
  • integrated systems
  • Internet-of-Things
  • non-destructive
  • Point-of-Test
  • precision agriculture
  • proximal sensing
  • quality monitoring
  • remote sensing
  • sensor

Published Papers (21 papers)

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15 pages, 5545 KiB  
Article
An Approach towards Motion-Tolerant PPG-Based Algorithm for Real-Time Heart Rate Monitoring of Moving Pigs
by Ali Youssef, Alberto Peña Fernández, Laura Wassermann, Svenja Biernot, Eva-Maria Wittauer, André Bleich, Joerg Hartung, Daniel Berckmans and Tomas Norton
Sensors 2020, 20(15), 4251; https://0-doi-org.brum.beds.ac.uk/10.3390/s20154251 - 30 Jul 2020
Cited by 11 | Viewed by 3761
Abstract
Animal welfare remains a very important issue in the livestock sector, but monitoring animal welfare in an objective and continuous way remains a serious challenge. Monitoring animal welfare, based upon physiological measurements instead of the audio–visual scoring of behaviour, would be a step [...] Read more.
Animal welfare remains a very important issue in the livestock sector, but monitoring animal welfare in an objective and continuous way remains a serious challenge. Monitoring animal welfare, based upon physiological measurements instead of the audio–visual scoring of behaviour, would be a step forward. One of the obvious physiological signals related to welfare and stress is heart rate. The objective of this research was to measure heart rate (beat per minutes) in pigs with technology that soon will be affordable. Affordable heart rate monitoring is done today at large scale on humans using the Photo Plethysmography (PPG) technology. We used PPG sensors on a pig′s body to test whether it allows the retrieval of a reliable heart rate signal. A continuous wavelet transform (CWT)-based algorithm is developed to decouple the cardiac pulse waves from the pig. Three different wavelets, namely second, fourth and sixth order Derivative of Gaussian (DOG), are tested. We show the results of the developed PPG-based algorithm, against electrocardiograms (ECG) as a reference measure for heart rate, and this for an anaesthetised versus a non-anaesthetised animal. We tested three different anatomical body positions (ear, leg and tail) and give results for each body position of the sensor. In summary, it can be concluded that the agreement between the PPG-based heart rate technique and the reference sensor is between 91% and 95%. In this paper, we showed the potential of using the PPG-based technology to assess the pig′s heart rate. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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20 pages, 5492 KiB  
Article
Monitoring of Cow Location in a Barn by an Open-Source, Low-Cost, Low-Energy Bluetooth Tag System
by Victor Bloch and Matti Pastell
Sensors 2020, 20(14), 3841; https://0-doi-org.brum.beds.ac.uk/10.3390/s20143841 - 09 Jul 2020
Cited by 18 | Viewed by 4472
Abstract
Indoor localization of dairy cows is important for cow behavior recognition and effective farm management. In this paper, we propose a low-cost system for low-accuracy cow localization based on the reception of signals sent by an acceleration measurement system using the Bluetooth Low [...] Read more.
Indoor localization of dairy cows is important for cow behavior recognition and effective farm management. In this paper, we propose a low-cost system for low-accuracy cow localization based on the reception of signals sent by an acceleration measurement system using the Bluetooth Low Energy protocol. The system consists of low-cost tags and receiving stations. The tag specifications and the localization accuracy of the system were studied experimentally. The received signal strength propagation model and dependence on the tag orientation was studied in an open-space and a barn environment. Two experiments for the evaluation of localization accuracy were conducted with 35 and 19 cows for two days. The localization reference was achieved from feeding stations, a milking robot and videos of cows decoded manually. The localization accuracy (mean ± standard deviation) was 3.27 ± 2.11 m for the entire barn (10 × 40 m2) and 1.9 ± 0.67 m for a smaller area (4 × 5 m2). The system can be used for recognizing long-distance walking, crowded areas in the barn, e.g., queues to milking robots, and cow’s preferable locations. The estimated system cost was 500 + 20 × (cow number) € for one barn. The system has open-access software and detailed instructions for its installation and usage. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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21 pages, 3718 KiB  
Article
Assessing the Activity of Individual Group-Housed Broilers Throughout Life Using a Passive Radio Frequency Identification System—A Validation Study
by Malou van der Sluis, Yvette de Haas, Britt de Klerk, T. Bas Rodenburg and Esther D. Ellen
Sensors 2020, 20(13), 3612; https://0-doi-org.brum.beds.ac.uk/10.3390/s20133612 - 27 Jun 2020
Cited by 20 | Viewed by 3744
Abstract
Individual data are valuable for assessing the health, welfare and performance of broilers. In particular, data on the first few days of life are needed to study the predictive value of traits recorded early in life for later life performance. However, broilers are [...] Read more.
Individual data are valuable for assessing the health, welfare and performance of broilers. In particular, data on the first few days of life are needed to study the predictive value of traits recorded early in life for later life performance. However, broilers are generally kept in groups, which hampers individual identification and monitoring of animals. Sensor technologies may aid in identifying and monitoring individual animals. In this study, a passive radio frequency identification (RFID) system was implemented to record broiler activity, in combination with traditional video recordings. The two main objectives were (1) to validate the output of the RFID system by comparing it to the recorded locations on video, and (2) to assess whether the number of antennas visited per unit time could serve as a measure of activity, by comparing it to the distance recorded on video and to the distance moved as recorded using a validated ultra-wideband (UWB) tracking system. The locations recorded by the RFID system exactly matched the video in 62.5% of the cases, and in 99.2% of the cases when allowing for a deviation of one antenna grid cell. There were moderately strong Spearman rank correlations between the distance recorded with the RFID system and the distance recorded from video (rs = 0.82) and between UWB and RFID (rs = 0.70) in approximately one-hour recordings, indicating that the RFID system can adequately track relative individual broiler activity, i.e., the activity level of a broiler in comparison to its group members. As the RFID tags are small and lightweight, the RFID system is well suited for monitoring the individual activity of group-housed broilers throughout life. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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18 pages, 19377 KiB  
Article
Automatic Cow Location Tracking System Using Ear Tag Visual Analysis
by Thi Thi Zin, Moe Zet Pwint, Pann Thinzar Seint, Shin Thant, Shuhei Misawa, Kosuke Sumi and Kyohiro Yoshida
Sensors 2020, 20(12), 3564; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123564 - 23 Jun 2020
Cited by 21 | Viewed by 12575
Abstract
Nowadays, for numerous reasons, smart farming systems focus on the use of image processing technologies and 5G communications. In this paper, we propose a tracking system for individual cows using an ear tag visual analysis. By using ear tags, the farmers can track [...] Read more.
Nowadays, for numerous reasons, smart farming systems focus on the use of image processing technologies and 5G communications. In this paper, we propose a tracking system for individual cows using an ear tag visual analysis. By using ear tags, the farmers can track specific data for individual cows such as body condition score, genetic abnormalities, etc. Specifically, a four-digit identification number is used, so that a farm can accommodate up to 9999 cows. In our proposed system, we develop an individual cow tracker to provide effective management with real-time upgrading enforcement. For this purpose, head detection is first carried out to determine the cow’s position in its related camera view. The head detection process incorporates an object detector called You Only Look Once (YOLO) and is then followed by ear tag detection. The steps involved in ear tag recognition are (1) finding the four-digit area, (2) digit segmentation using an image processing technique, and (3) ear tag recognition using a convolutional neural network (CNN) classifier. Finally, a location searching system for an individual cow is established by entering the ID numbers through the application’s user interface. The proposed searching system was confirmed by performing real-time experiments at a feeding station on a farm at Hokkaido prefecture, Japan. In combination with our decision-making process, the proposed system achieved an accuracy of 100% for head detection, and 92.5% for ear tag digit recognition. The results of using our system are very promising in terms of effectiveness. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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20 pages, 6713 KiB  
Article
Biot-Granier Sensor: A Novel Strategy to Measuring Sap Flow in Trees
by Jucilene M. Siqueira, Teresa A. Paço, José Machado da Silva and José C. Silvestre
Sensors 2020, 20(12), 3538; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123538 - 22 Jun 2020
Cited by 4 | Viewed by 3842
Abstract
The Biot-Granier (Gbt) is a new thermal dissipation-based sap flow measurement methodology, comprising sensors, data management and automatic data processing. It relies on the conventional Granier (Gcv) methodology upgraded with a modified Granier sensor set, as well as on an algorithm to measure [...] Read more.
The Biot-Granier (Gbt) is a new thermal dissipation-based sap flow measurement methodology, comprising sensors, data management and automatic data processing. It relies on the conventional Granier (Gcv) methodology upgraded with a modified Granier sensor set, as well as on an algorithm to measure the absolute temperatures in the two observation points and perform the Biot number approach. The work described herein addresses the construction details of the Gbt sensors and the characterization of the overall performance of the Gbt method after comparison with a commercial sap flow sensor and independent data (i.e., volumetric water content, vapor pressure deficit and eddy covariance technique). Its performance was evaluated in three trials: potted olive trees in a greenhouse and two vineyards. The trial with olive trees in a greenhouse showed that the transpiration measures provided by the Gbt sensors showed better agreement with the gravimetric approach, compared to those provided by the Gcv sensors. These tended to overestimate sap flow rates as much as 4 times, while Gbt sensors overestimated gravimetric values 1.5 times. The adjustments based on the Biot equations obtained with Gbt sensors contribute to reduce the overestimates yielded by the conventional approach. On the other hand, the heating capacity of the Gbt sensor provided a minimum of around 7 °C and maximum about 9 °C, contrasting with a minimum around 6 °C and a maximum of 12 °C given by the Gcv sensors. The positioning of the temperature sensor on the tip of the sap flow needle proposed in the Gbt sensors, closer to the sap measurement spot, allow to capture sap induced temperature variations more accurately. This explains the higher resolution and sensitivity of the Gbt sensor. Overall, the alternative Biot approach showed a significant improvement in sap flow estimations, contributing to adjust the Granier sap flow index, a vulnerability of that methodology. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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19 pages, 12127 KiB  
Article
Applying Infrared Thermography to Soil Surface Temperature Monitoring: Case Study of a High-Resolution 48 h Survey in a Vineyard (Anadia, Portugal)
by William Frodella, Giacomo Lazzeri, Sandro Moretti, Jacob Keizer and Frank G. A. Verheijen
Sensors 2020, 20(9), 2444; https://0-doi-org.brum.beds.ac.uk/10.3390/s20092444 - 25 Apr 2020
Cited by 11 | Viewed by 4704
Abstract
The soil surface albedo decreases with an increasing biochar application rate as a power decay function, but the net impact of biochar application on soil temperature dynamics remains to be clarified. The objective of this study was to assess the potential of infrared [...] Read more.
The soil surface albedo decreases with an increasing biochar application rate as a power decay function, but the net impact of biochar application on soil temperature dynamics remains to be clarified. The objective of this study was to assess the potential of infrared thermography (IRT) sensing by monitoring soil surface temperature (SST) with a high spatiotemporal and thermal resolution in a scalable agricultural application. We monitored soil surface temperature (SST) variations over a 48 h period for three treatments in a vineyard: bare soil (plot S), 100% biochar cover (plot B), and biochar-amended topsoil (plot SB). The SST of all plots was monitored at 30 min intervals with a tripod-mounted IR thermal camera. The soil temperature at 10 cm depth in the S and SB plots was monitored continuously with a 5 min resolution probe. Plot B had greater daily SST variations, reached a higher daily temperature peak relative to the other plots, and showed a faster rate of T increase during the day. However, on both days, the SST of plot B dipped below that of the control treatment (plot S) and biochar-amended soil (plot SB) from about 18:00 onward and throughout the night. The diurnal patterns/variations in the IRT-measured SSTs were closely related to those in the soil temperature at a 10 cm depth, confirming that biochar-amended soils showed lower thermal inertia than the unamended soil. The experiment provided interesting insights into SST variations at a local scale. The case study may be further developed using fully automated SST monitoring protocols at a larger scale for a range of environmental and agricultural applications. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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12 pages, 3460 KiB  
Article
An All-Solid-State Nitrate Ion-Selective Electrode with Nanohybrids Composite Films for In-Situ Soil Nutrient Monitoring
by Ming Chen, Miao Zhang, Xuming Wang, Qingliang Yang, Maohua Wang, Gang Liu and Lan Yao
Sensors 2020, 20(8), 2270; https://0-doi-org.brum.beds.ac.uk/10.3390/s20082270 - 16 Apr 2020
Cited by 30 | Viewed by 4466
Abstract
In this paper, an all-solid-state nitrate doped polypyrrole (PPy(NO3−) ion-selective electrode (ISE) was prepared with a nanohybrid composite film of gold nanoparticles (AuNPs) and electrochemically reduced graphene oxide (ERGO). Preliminary tests on the ISE based in-situ soil nitrate–nitrogen (NO3 [...] Read more.
In this paper, an all-solid-state nitrate doped polypyrrole (PPy(NO3−) ion-selective electrode (ISE) was prepared with a nanohybrid composite film of gold nanoparticles (AuNPs) and electrochemically reduced graphene oxide (ERGO). Preliminary tests on the ISE based in-situ soil nitrate–nitrogen (NO3−-N) monitoring was conducted in a laboratory 3-stage column. Comparisons were made between the NO3−-N content of in-situ soil percolate solution and laboratory-prepared extract solution. Possible influential factors of sample depth, NO3−-N content, soil texture, and moisture were varied. Field-emission scanning electron microscopy (FESEM) and X-ray powder diffraction (XRD) characterized morphology and content information of the composite film of ERGO/AuNPs. Due to the performance excellence for conductivity, stability, and hydrophobicity, the ISE with ERGO/AuNPs illustrates an acceptable detection range from 10−1 to 10−5 M. The response time was determined to be about 10 s. The lifetime was 65 days, which revealed great potential for the implementation of the ERGO/AuNPs mediated ISE for in-situ NO3−-N monitoring. In-situ NO3−-N testing results conducted by the all-solid-state ISE followed a similar trend with the standard UV-VIS method. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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21 pages, 6992 KiB  
Article
Monitoring Plant Status and Fertilization Strategy through Multispectral Images
by Matheus Cardim Ferreira Lima, Anne Krus, Constantino Valero, Antonio Barrientos, Jaime del Cerro and Juan Jesús Roldán-Gómez
Sensors 2020, 20(2), 435; https://0-doi-org.brum.beds.ac.uk/10.3390/s20020435 - 13 Jan 2020
Cited by 26 | Viewed by 7578
Abstract
A crop monitoring system was developed for the supervision of organic fertilization status on tomato plants at early stages. An automatic and nondestructive approach was used to analyze tomato plants with different levels of water-soluble organic fertilizer (3 + 5 NK) and vermicompost. [...] Read more.
A crop monitoring system was developed for the supervision of organic fertilization status on tomato plants at early stages. An automatic and nondestructive approach was used to analyze tomato plants with different levels of water-soluble organic fertilizer (3 + 5 NK) and vermicompost. The evaluation system was composed by a multispectral camera with five lenses: green (550 nm), red (660 nm), red edge (735 nm), near infrared (790 nm), RGB, and a computational image processing system. The water-soluble fertilizer was applied weekly in four different treatments: (T0: 0 mL, T1: 6.25 mL, T2: 12.5 mL and T3: 25 mL) and the vermicomposting was added in Weeks 1 and 5. The trial was conducted in a greenhouse and 192 images were taken with each lens. A plant segmentation algorithm was developed and several vegetation indices were calculated. On top of calculating indices, multiple morphological features were obtained through image processing techniques. The morphological features were revealed to be more feasible to distinguish between the control and the organic fertilized plants than the vegetation indices. The system was developed in order to be assembled in a precision organic fertilization robotic platform. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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19 pages, 12242 KiB  
Article
Design and Testing of a Structural Monitoring System in an Almería-Type Tensioned Structure Greenhouse
by Araceli Peña, Mercedes Peralta and Patricia Marín
Sensors 2020, 20(1), 258; https://0-doi-org.brum.beds.ac.uk/10.3390/s20010258 - 01 Jan 2020
Cited by 5 | Viewed by 3737
Abstract
Greenhouse cultivation has gained a special importance in recent years and become the basis of the economy in south-eastern Spain. The structures used are light and, due to weather events, often collapse completely or partially, which has generated interest in the study of [...] Read more.
Greenhouse cultivation has gained a special importance in recent years and become the basis of the economy in south-eastern Spain. The structures used are light and, due to weather events, often collapse completely or partially, which has generated interest in the study of these unique buildings. This study presents a load and displacement monitoring system that was designed, and full scale tested, in an Almería-type greenhouse with a tensioned wire structure. The loads and displacements measured under real load conditions were recorded for multiple time periods. The traction force on the roof cables decreased up to 22% for a temperature increase of 30 °C, and the compression force decreased up to 16.1% on the columns or pillars for a temperature and wind speed increase of 25.8 °C and 1.9 m/s respectively. The results show that the structure is susceptible to daily temperature changes and, to a lesser extent, wind throughout the test. The monitoring system, which uses load cells to measure loads and machine vision techniques to measure displacements, is appropriate for use in different types of greenhouses. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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19 pages, 5029 KiB  
Article
A Comparative Assessment of Measures of Leaf Nitrogen in Rice Using Two Leaf-Clip Meters
by Ke Zhang, Xiaojun Liu, Yong Ma, Rui Zhang, Qiang Cao, Yan Zhu, Weixing Cao and Yongchao Tian
Sensors 2020, 20(1), 175; https://0-doi-org.brum.beds.ac.uk/10.3390/s20010175 - 27 Dec 2019
Cited by 26 | Viewed by 4132
Abstract
Accurate estimation and monitoring of crop nitrogen can assist in timely diagnosis and facilitate necessary technical support for fertilizer management. Four experiments, involving three cultivars and six nitrogen (N) treatments, were conducted in southeast China to compare the two leaf-clip meters (Dualex 4 [...] Read more.
Accurate estimation and monitoring of crop nitrogen can assist in timely diagnosis and facilitate necessary technical support for fertilizer management. Four experiments, involving three cultivars and six nitrogen (N) treatments, were conducted in southeast China to compare the two leaf-clip meters (Dualex 4 Scientific+, Force-A, Orasy, France; Soil and Plant Analyzer Development (SPAD) meter, Minolta Camera Co., Osaka, Japan) for their ability to measure nitrogen nutrient-related indicators. The results indicated that Chl had a better monitoring accuracy for chlorophyll in per unit leaf area as compared to SPAD value, and there was no saturation to appear under high leaf chlorophyll concentration status. Flavonoids (Flav) presented the advantage of early diagnosis of rice N nutrition status (about one day as compared to SPAD value). As a reliable N nutrient diagnosis indicator, it also improved the estimation accuracy compared with the classical SPAD-based method. The other Dualex value also obtained good monitoring results. Flav was positively correlated with N deficiency, and with higher R2 in panicle initiation and booting stages with low RMSE, respectively; whereas SPAD value was negatively correlated with nitrogen deficiency. Therefore, the Flav-based nitrogen application model was found to provide an early rice nitrogen fertilizer application approach, especially in the panicle initiation and booting stages. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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19 pages, 7893 KiB  
Article
An Integrated Method for Coding Trees, Measuring Tree Diameter, and Estimating Tree Positions
by Linhao Sun, Luming Fang, Yuhui Weng and Siqing Zheng
Sensors 2020, 20(1), 144; https://0-doi-org.brum.beds.ac.uk/10.3390/s20010144 - 24 Dec 2019
Cited by 11 | Viewed by 4248
Abstract
Accurately measuring tree diameter at breast height (DBH) and estimating tree positions in a sample plot are important in tree mensuration. The main aims of this paper include (1) developing a new, integrated device that can identify trees using the quick response (QR) [...] Read more.
Accurately measuring tree diameter at breast height (DBH) and estimating tree positions in a sample plot are important in tree mensuration. The main aims of this paper include (1) developing a new, integrated device that can identify trees using the quick response (QR) code technique to record tree identifications, measure DBH, and estimate tree positions concurrently; (2) designing an innovative algorithm to measure DBH using only two angle sensors, which is simple and can reduce the impact of eccentric stems on DBH measures; and (3) designing an algorithm to estimate the position of the tree by combining ultra-wide band (UWB) technology and altitude sensors, which is based on the received signal strength indication (RSSI) algorithm and quadrilateral localization algorithm. This novel device was applied to measure ten 10 × 10 m square plots of diversified environments and various tree species to test its accuracy. Before measuring a plot, a coded sticker was fixed at a height of 1.3 m on each individual tree stem, and four UWB module anchors were set up at the four corners of the plot. All individual trees’ DBHs and positions within the plot were then measured. Tree DBH, measured using a tree caliper, and the values of tree positions, measured using tape, angle ruler, and inclinometer, were used as the respective reference values for comparison. Across the plots, the decode rate of QR codes was 100%, with an average response time less than two seconds. The DBH values had a bias of 1.89 mm (1.88% in relative terms) and a root mean square error (RMSE) of 5.38 mm (4.53% in relative terms). The tree positions were accurately estimated; the biases on the x-axis and the y-axis of the tree position were −8.55–14.88 cm and −12.07–24.49 cm, respectively, and the corresponding RMSEs were 12.94–33.96 cm and 17.78–28.43 cm. The average error between the estimated and reference distances was 30.06 cm, with a standard deviation of 13.53 cm. The device is cheap and friendly to use in addition to its high accuracy. Although further studies are needed, our method provides a great alternative to conventional tools for improving the efficiency and accuracy of tree mensuration. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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18 pages, 4989 KiB  
Article
Development and Experimental Analysis of a Seeding Quantity Sensor for the Precision Seeding of Small Seeds
by Wei Liu, Jianping Hu, Xingsheng Zhao, Haoran Pan, Imran Ali Lakhiar, Wei Wang and Jun Zhao
Sensors 2019, 19(23), 5191; https://0-doi-org.brum.beds.ac.uk/10.3390/s19235191 - 27 Nov 2019
Cited by 8 | Viewed by 5251
Abstract
Having the correct seeding rate for a unit area is vital to crop yields. In order to assess the desirable seeding rate, the number of discharged seeds needs to be monitored in real-time. However, for small seeds, the miscounting of seeds during monitoring [...] Read more.
Having the correct seeding rate for a unit area is vital to crop yields. In order to assess the desirable seeding rate, the number of discharged seeds needs to be monitored in real-time. However, for small seeds, the miscounting of seeds during monitoring happens frequently when using conventional seeding quantity sensors, which have wide light beam intervals. Thus, a seeding quantity sensor, which enables small seeds to pass through the light beam steadily, was developed. Based on the seed-shading time, a seed-counting algorithm was proposed. Moreover, the key structure parameters of the proposed sensor were ascertained using an optimization experiment. Finally, the developed seeding quantity sensor was tested against a photoelectric sensor and a fiber sensor to compare the seed monitoring accuracies. The results show that the average monitoring accuracy of the developed sensor, photoelectric sensor, and fiber sensor were 97.09%, 56.79%, and 91.10%, respectively. Furthermore, the factorial analysis shows that the forward velocity of the experimental apparatus and the rotational speed of the seeding plate did not significantly change the monitoring accuracies obtained by the developed sensor. Therefore, the developed sensor can be applied to monitor the seed quantity for the precision seeding of small seeds accurately and robustly. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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13 pages, 2339 KiB  
Article
Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine
by Hongyang Li, Shengyao Jia and Zichun Le
Sensors 2019, 19(20), 4355; https://0-doi-org.brum.beds.ac.uk/10.3390/s19204355 - 09 Oct 2019
Cited by 44 | Viewed by 4349
Abstract
Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900–1700 nm using a hyperspectral imaging [...] Read more.
Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900–1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction effect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE–ELM model performed better, having a correlation coefficient of prediction (rp), root-mean-square error of prediction (RMSEP), and residual prediction deviation (RPD) of 0.9408, 0.0075, and 2.97, respectively. The UVE–ELM model was then used to estimate the TN content in the soil sample and obtain a distribution map. The research results indicate that HSI can be used for the detection and visualization of the distribution of TN content in soil, providing a basis for future large-scale monitoring of soil nutrient distribution and rational fertilization. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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32 pages, 19890 KiB  
Article
A Novel, Portable and Fast Moisture Content Measuring Method for Grains Based on an Ultra-Wideband (UWB) Radar Module and the Mode Matching Method
by Chi Zhang, Zhichao Shi, Haiying Yang, Xiaoguang Zhou, Zidan Wu and Digvir S. Jayas
Sensors 2019, 19(19), 4224; https://0-doi-org.brum.beds.ac.uk/10.3390/s19194224 - 28 Sep 2019
Cited by 12 | Viewed by 3306
Abstract
To perform fast and portable grain moisture measurements under field conditions, a novel moisture sensor was designed, which consisted of a coaxial waveguide, a circular waveguide, and an isolation layer. The electromagnetic characteristics of the sensor were simulated and measured. The analytical model, [...] Read more.
To perform fast and portable grain moisture measurements under field conditions, a novel moisture sensor was designed, which consisted of a coaxial waveguide, a circular waveguide, and an isolation layer. The electromagnetic characteristics of the sensor were simulated and measured. The analytical model, which represented the relationship between the reflection coefficient of the sensor and the complex permittivity of grain, was established by using the mode matching method. The reflection coefficient of the sensor was measured by using an ultra-wideband (UWB) radar module, and the moisture content of grains was calculated from the complex permittivity by using density-independent model. To verify the performance of the proposed method, wheat, rough rice, and barley were taken as examples. The measured results in the range from 1.0% to 26.0%, wet basis, agreed well with the reference values (R2 was more than 0.99), and the maximum absolute errors for wheat, rough rice, and barley were 1.1%, 1.0%, and 1.4%, respectively. In addition, the effect of isolation layer was discussed. Both the simulation results and the experimental results showed that the isolation layer improved the stability of sensor. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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14 pages, 3666 KiB  
Article
Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network
by Jie Hang, Dexiang Zhang, Peng Chen, Jun Zhang and Bing Wang
Sensors 2019, 19(19), 4161; https://0-doi-org.brum.beds.ac.uk/10.3390/s19194161 - 25 Sep 2019
Cited by 99 | Viewed by 8981
Abstract
Plant leaf diseases are closely related to people’s daily life. Due to the wide variety of diseases, it is not only time-consuming and labor-intensive to identify and classify diseases by artificial eyes, but also easy to be misidentified with having a high error [...] Read more.
Plant leaf diseases are closely related to people’s daily life. Due to the wide variety of diseases, it is not only time-consuming and labor-intensive to identify and classify diseases by artificial eyes, but also easy to be misidentified with having a high error rate. Therefore, we proposed a deep learning-based method to identify and classify plant leaf diseases. The proposed method can take the advantages of the neural network to extract the characteristics of diseased parts, and thus to classify target disease areas. To address the issues of long training convergence time and too-large model parameters, the traditional convolutional neural network was improved by combining a structure of inception module, a squeeze-and-excitation (SE) module and a global pooling layer to identify diseases. Through the Inception structure, the feature data of the convolutional layer were fused in multi-scales to improve the accuracy on the leaf disease dataset. Finally, the global average pooling layer was used instead of the fully connected layer to reduce the number of model parameters. Compared with some traditional convolutional neural networks, our model yielded better performance and achieved an accuracy of 91.7% on the test data set. At the same time, the number of model parameters and training time have also been greatly reduced. The experimental classification on plant leaf diseases indicated that our method is feasible and effective. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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16 pages, 2321 KiB  
Article
Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
by Dong Han, Shuaibing Liu, Ying Du, Xinrui Xie, Lingling Fan, Lei Lei, Zhenhong Li, Hao Yang and Guijun Yang
Sensors 2019, 19(18), 4013; https://0-doi-org.brum.beds.ac.uk/10.3390/s19184013 - 17 Sep 2019
Cited by 33 | Viewed by 4065
Abstract
This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models [...] Read more.
This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models for winter wheat crop water content, respectively. In the Sentinel-1 approach, several enhanced radar indices were constructed by Sentinel-1 backscatter coefficient of imagery, and selected the one that was most sensitive to soil water content as the input parameter of a water cloud model. Finally, a water content inversion model for winter wheat crop was established. In the Sentinel-2 approach, the gray relational analysis was used for several optical vegetation indices constructed by Sentinel-2 spectral feature of imagery, and three vegetation indices were selected for multiple linear regression modeling to retrieve the wheat crop water content. 58 ground samples were utilized in modeling and verification. The water content inversion model based on Sentinel-2 optical images exhibited higher verification accuracy (R = 0.632, RMSE = 0.021 and nRMSE = 19.65%) than the inversion model based on Sentinel-1 SAR (R = 0.433, RMSE = 0.026 and nRMSE = 21.24%). This study provides a reference for estimating the water content of wheat crops using data from the Sentinel series of satellites. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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17 pages, 3263 KiB  
Article
Measurement Method Based on Multispectral Three-Dimensional Imaging for the Chlorophyll Contents of Greenhouse Tomato Plants
by Guoxiang Sun, Xiaochan Wang, Ye Sun, Yongqian Ding and Wei Lu
Sensors 2019, 19(15), 3345; https://0-doi-org.brum.beds.ac.uk/10.3390/s19153345 - 30 Jul 2019
Cited by 22 | Viewed by 4402
Abstract
Nondestructive plant growth measurement is essential for researching plant growth and health. A nondestructive measurement system to retrieve plant information includes the measurement of morphological and physiological information, but most systems use two independent measurement systems for the two types of characteristics. In [...] Read more.
Nondestructive plant growth measurement is essential for researching plant growth and health. A nondestructive measurement system to retrieve plant information includes the measurement of morphological and physiological information, but most systems use two independent measurement systems for the two types of characteristics. In this study, a highly integrated, multispectral, three-dimensional (3D) nondestructive measurement system for greenhouse tomato plants was designed. The system used a Kinect sensor, an SOC710 hyperspectral imager, an electric rotary table, and other components. A heterogeneous sensing image registration technique based on the Fourier transform was proposed, which was used to register the SOC710 multispectral reflectance in the Kinect depth image coordinate system. Furthermore, a 3D multiview RGB-D image-reconstruction method based on the pose estimation and self-calibration of the Kinect sensor was developed to reconstruct a multispectral 3D point cloud model of the tomato plant. An experiment was conducted to measure plant canopy chlorophyll and the relative chlorophyll content was measured by the soil and plant analyzer development (SPAD) measurement model based on a 3D multispectral point cloud model and a single-view point cloud model and its performance was compared and analyzed. The results revealed that the measurement model established by using the characteristic variables from the multiview point cloud model was superior to the one established using the variables from the single-view point cloud model. Therefore, the multispectral 3D reconstruction approach is able to reconstruct the plant multispectral 3D point cloud model, which optimizes the traditional two-dimensional image-based SPAD measurement method and can obtain a precise and efficient high-throughput measurement of plant chlorophyll. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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11 pages, 2142 KiB  
Article
Effect of the Architecture of Fiber-Optic Probes Designed for Soluble Solid Content Prediction in Intact Sugar Beet Slices
by Ryad Bendoula, Arnaud Ducanchez, Ana Herrero-Langreo, Pablo Guerrero-Castro, Jean-Michel Roger and Alexia Gobrecht
Sensors 2019, 19(13), 2995; https://0-doi-org.brum.beds.ac.uk/10.3390/s19132995 - 07 Jul 2019
Cited by 2 | Viewed by 2882
Abstract
Sugar beet is the second biggest world contributor to sugar production and the only one grown in Europe. One of the main limitations for its competitiveness is the lack of effective tools for assessing sugar content in unprocessed sugar beet roots, especially in [...] Read more.
Sugar beet is the second biggest world contributor to sugar production and the only one grown in Europe. One of the main limitations for its competitiveness is the lack of effective tools for assessing sugar content in unprocessed sugar beet roots, especially in breeding programs. In this context, a dedicated near infrared (NIR) fiber-optic probe based approach is proposed. NIR technology is widely used for the estimation of sugar content in vegetable products, while optic fibers allow a wide choice of technical properties and configurations. The objective of this research was to study the best architecture through different technical choices for the estimation of sugar content in intact sugar beet roots. NIR spectral measurements were taken on unprocessed sugar beet samples using two types of geometries, single and multiple fiber-probes. Sugar content estimates were more accurate when using multiple fiber-probes (up to R2 = 0.93) due to a lesser disruption of light specular reflection. In turn, on this configuration, the best estimations were observed for the smallest distances between emitting and collecting fibers, reducing the proportion of multiply scattered light in the spectra. Error of prediction (RPD) values of 3.95, 3.27 and 3.09 were obtained for distances between emitting and collecting fibers of 0.6, 1.2 and 1.8 µm respectively. These high RPD values highlight the good predictions capacities of the multi-fiber probes. Finally, this study contributes to a better understanding of the effects of the technical properties of optical fiber-probes on the quality of spectral models. In addition, and beyond this specificity related to sugar beet, these findings could be extended to other turbid media for quantitative optical spectroscopy and eventually to validate considered fiber-optic probe design obtained in this experimental study. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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17 pages, 8318 KiB  
Article
Hyperspectral-Based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables
by Lingling Fan, Jinling Zhao, Xingang Xu, Dong Liang, Guijun Yang, Haikuan Feng, Hao Yang, Yulong Wang, Guo Chen and Pengfei Wei
Sensors 2019, 19(13), 2898; https://0-doi-org.brum.beds.ac.uk/10.3390/s19132898 - 30 Jun 2019
Cited by 42 | Viewed by 4059
Abstract
Accurate and dynamic monitoring of crop nitrogen status is the basis of scientific decisions regarding fertilization. In this study, we compared and analyzed three types of spectral variables: Sensitive spectral bands, the position of spectral features, and typical hyperspectral vegetation indices. First, the [...] Read more.
Accurate and dynamic monitoring of crop nitrogen status is the basis of scientific decisions regarding fertilization. In this study, we compared and analyzed three types of spectral variables: Sensitive spectral bands, the position of spectral features, and typical hyperspectral vegetation indices. First, the Savitzky-Golay technique was used to smooth the original spectrum, following which three types of spectral parameters describing crop spectral characteristics were extracted. Next, the successive projections algorithm (SPA) was adopted to screen out the sensitive variable set from each type of parameters. Finally, partial least squares (PLS) regression and random forest (RF) algorithms were used to comprehensively compare and analyze the performance of different types of spectral variables for estimating corn leaf nitrogen content (LNC). The results show that the integrated variable set composed of the optimal ones screened by SPA from three types of variables had the best performance for LNC estimation by the validation data set, with the values of R2, root means square error (RMSE), and normalized root mean square error (NRMSE) of 0.77, 0.31, and 17.1%, and 0.55, 0.43, and 23.9% from PLS and RF, respectively. It indicates that the PLS model with optimally multitype spectral variables can provide better fits and be a more effective tool for evaluating corn LNC. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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11 pages, 4085 KiB  
Article
Mini Inside-Out Nuclear Magnetic Resonance Sensor Design for Soil Moisture Measurements
by Jiamin Wu, Pan Guo, Sheng Shen, Yucheng He, Xin Huang and Zheng Xu
Sensors 2019, 19(7), 1682; https://0-doi-org.brum.beds.ac.uk/10.3390/s19071682 - 09 Apr 2019
Cited by 9 | Viewed by 3710
Abstract
The improvement of water management in agriculture by exactly detecting moisture parameters of soil is crucial. To investigate this problem, a mini inside-out nuclear magnetic resonance sensor (NMR) was proposed to measure moisture parameters of model soils. This sensor combines three cylindrical magnets [...] Read more.
The improvement of water management in agriculture by exactly detecting moisture parameters of soil is crucial. To investigate this problem, a mini inside-out nuclear magnetic resonance sensor (NMR) was proposed to measure moisture parameters of model soils. This sensor combines three cylindrical magnets that are magnetized in the axial direction and three arc spiral coils of the same size in series. We calculated and optimized the magnet structure by equivalent magnetization to current density. By adjusting the radius and height between the cylinders, a circumferential symmetric constant gradient field (2.28 T/m) was obtained. The NMR sensor was set at 2.424 MHz to measure the water content of sandy soil with small particle diameter and silica sand with large particle diameter. The complete decaying, an NMR signal was analyzed through inverse Laplace transformation and averaged on a T2 space. According to the results, moisture content of the sample is positively correlated with the integral area of T2 spectrum peak (Apeak); T2 of the water in small pores is shorter than that in large pores, because the movement of water molecules are limited by the inner wall of the pores. In the same volume, water in large pore sample is more than that in small pore sample, so Apeak of silica sand is larger than Apeak of sandy soil. Therefore, the sensor is capable of detecting moisture both content and pore size of the sample. This mini sensor (4.0 cm in diameter and 10 cm in length) is portable, and the lowest measurable humidity is 0.38%. Thus, this sensor will allow easy soil moisture measurements on-field in the future. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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Review

Jump to: Research

27 pages, 2325 KiB  
Review
Agricultural Robotics for Field Operations
by Spyros Fountas, Nikos Mylonas, Ioannis Malounas, Efthymios Rodias, Christoph Hellmann Santos and Erik Pekkeriet
Sensors 2020, 20(9), 2672; https://0-doi-org.brum.beds.ac.uk/10.3390/s20092672 - 07 May 2020
Cited by 154 | Viewed by 19522
Abstract
Modern agriculture is related to a revolution that occurred in a large group of technologies (e.g., informatics, sensors, navigation) within the last decades. In crop production systems, there are field operations that are quite labour-intensive either due to their complexity or because of [...] Read more.
Modern agriculture is related to a revolution that occurred in a large group of technologies (e.g., informatics, sensors, navigation) within the last decades. In crop production systems, there are field operations that are quite labour-intensive either due to their complexity or because of the fact that they are connected to sensitive plants/edible product interaction, or because of the repetitiveness they require throughout a crop production cycle. These are the key factors for the development of agricultural robots. In this paper, a systematic review of the literature has been conducted on research and commercial agricultural robotics used in crop field operations. This study underlined that the most explored robotic systems were related to harvesting and weeding, while the less studied were the disease detection and seeding robots. The optimization and further development of agricultural robotics are vital, and should be evolved by producing faster processing algorithms, better communication between the robotic platforms and the implements, and advanced sensing systems. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
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