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Special Issue "Advanced Sensors in Agriculture"

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

Deadline for manuscript submissions: 31 May 2020.

Special Issue Editor

Prof. Dr. Spyridon Kintzios
E-Mail Website
Guest Editor
Department of Biotechnology, Agricultural University of Athens, Athens, Greece
Tel. +302105294292
Interests: biosensors; biotechnology; cell culture; cell technology
Special Issues and Collections 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 1800 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 (8 papers)

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Research

Open AccessArticle
Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine
Sensors 2019, 19(20), 4355; https://doi.org/10.3390/s19204355 - 09 Oct 2019
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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Open AccessArticle
A Novel, Portable and Fast Moisture Content Measuring Method for Grains Based on an Ultra-Wideband (UWB) Radar Module and the Mode Matching Method
Sensors 2019, 19(19), 4224; https://doi.org/10.3390/s19194224 - 28 Sep 2019
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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Open AccessArticle
Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network
Sensors 2019, 19(19), 4161; https://doi.org/10.3390/s19194161 - 25 Sep 2019
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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Open AccessArticle
Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
Sensors 2019, 19(18), 4013; https://doi.org/10.3390/s19184013 - 17 Sep 2019
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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Open AccessArticle
Measurement Method Based on Multispectral Three-Dimensional Imaging for the Chlorophyll Contents of Greenhouse Tomato Plants
Sensors 2019, 19(15), 3345; https://doi.org/10.3390/s19153345 - 30 Jul 2019
Cited by 1
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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Open AccessArticle
Effect of the Architecture of Fiber-Optic Probes Designed for Soluble Solid Content Prediction in Intact Sugar Beet Slices
Sensors 2019, 19(13), 2995; https://doi.org/10.3390/s19132995 - 07 Jul 2019
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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Open AccessArticle
Hyperspectral-Based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables
Sensors 2019, 19(13), 2898; https://doi.org/10.3390/s19132898 - 30 Jun 2019
Cited by 2
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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Open AccessArticle
Mini Inside-Out Nuclear Magnetic Resonance Sensor Design for Soil Moisture Measurements
Sensors 2019, 19(7), 1682; https://doi.org/10.3390/s19071682 - 09 Apr 2019
Cited by 1
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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