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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 52953

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Special Issue Editor

Prof. Dr. Dimitrios Moshou

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Head of Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki (A.U.Th.), P.O. 275, 54124 Thessaloniki, Greece
Interests: remote sensing; multiscale fusion robotic agriculture; sensor networks; robotics; development of cognitive abilities; fusion of global and local cognition
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Special Issue Information

Dear Colleagues,

Agriculture requires technical solutions for increasing production while reducing environmental impact by reducing the application of agrochemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have facilitated the attainment of these objectives removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture is an emerging area where sensor-based technologies play an important role.

Farmers, researchers, and technical manufacturers are combining their efforts to find efficient solutions, improvements in production, and reductions in costs. This Special Issue aims to bring together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed. Papers addressing sensor development for a wide range of agricultural tasks, including, but not limited to, recent research and developments in the following areas are expected:

Optical sensors: Hyperspectral, multispectral, fluorescence, and thermal sensing
Sensors for crop health status determination
Sensors for crop phenotyping, germination, emergence, and determination of the different growth stages of crops
Sensors for the detection of microorganism and pest management
Airborne sensors (UAV)
Multisensor systems, sensor fusion
Non-destructive soil sensing
Yield estimation and prediction
Detection and identification of crops and weeds
Sensors for the detection of fruits
Sensors for fruit quality determination
Sensors for weed control
Volatile components detection, electronic noses, and tongues
Sensors for robot navigation, localization and mapping, and environmental awareness
Sensors for robotic applications in crop management
Sensors for positioning, navigation and obstacle detection
Sensor networks in agriculture, wearable sensors, the Internet of Things
Low energy, disposable, and energy harvesting sensors in agriculture
Deep learning from sensor data in agriculture

Prof. Dr. Dimitrios Moshou
Guest Editor

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Published Papers (11 papers)

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28 pages, 13309 KiB

Open AccessArticle

A Cloud-Based IoT Platform for Precision Control of Soilless Greenhouse Cultivation

by Alaa Sagheer, Maged Mohammed, Khaled Riad and Mohammed Alhajhoj

Sensors 2021, 21(1), 223; https://doi.org/10.3390/s21010223 - 31 Dec 2020

Cited by 46 | Viewed by 7798

Abstract

Food security has become an increasingly important challenge for all countries globally, particularly as the world population continues to grow and arable lands are diminishing due to urbanization. Water scarcity and lack of labor add extra negative influence on traditional agriculture and food production. The problem is getting worse in countries with arid lands and harsh climate, which exacerbates the food gap in these countries. Therefore, smart and practical solutions to promote cultivation and combat food production challenges are highly needed. As a controllable environment, greenhouses are the perfect environment to improve crops’ production and quality in harsh climate regions. Monitoring and controlling greenhouse microclimate is a real problem where growers have to deal with various parameters to ensure the optimal growth of crops. This paper shows our research in which we established a multi-tier cloud-based Internet of Things (IoT) platform to enhance the greenhouse microclimate. As a case study, we applied the IoT platform on cucumber cultivation in a soilless medium inside a commercial-sized greenhouse. The established platform connected all sensors, controllers, and actuators placed in the greenhouse to provide long-distance communication to monitor, control, and manage the greenhouse. The implemented platform increased the cucumber yield and enhanced its quality. Moreover, the platform improved water use efficiency and decreased consumption of electrical energy. Based on the positive impact on water use efficiency and enhancement on cucumber fruit yield and quality, the established platform seems quite suitable for the soilless greenhouse cultivation in arid regions. Full article

(This article belongs to the Special Issue Sensors in Agriculture 2020)

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15 pages, 3957 KiB

Open AccessArticle

UAV-Based RGB Imagery for Hokkaido Pumpkin (Cucurbita max.) Detection and Yield Estimation

by Lucas Wittstruck, Insa Kühling, Dieter Trautz, Maik Kohlbrecher and Thomas Jarmer

Sensors 2021, 21(1), 118; https://doi.org/10.3390/s21010118 - 27 Dec 2020

Cited by 21 | Viewed by 4118

Abstract

Pumpkins are economically and nutritionally valuable vegetables with increasing popularity and acreage across Europe. Successful commercialization, however, require detailed pre-harvest information about number and weight of the fruits. To get a non-destructive and cost-effective yield estimation, we developed an image processing methodology for high-resolution RGB data from Unmanned aerial vehicle (UAV) and applied this on a Hokkaido pumpkin farmer’s field in North-western Germany. The methodology was implemented in the programming language Python and comprised several steps, including image pre-processing, pixel-based image classification, classification post-processing for single fruit detection, and fruit size and weight quantification. To derive the weight from two-dimensional imagery, we calculated elliptical spheroids from lengths of diameters and heights. The performance of this processes was evaluated by comparison with manually harvested ground-truth samples and cross-checked for misclassification from randomly selected test objects. Errors in classification and fruit geometry could be successfully reduced based on the described processing steps. Additionally, different lighting conditions, as well as shadows, in the image data could be compensated by the proposed methodology. The results revealed a satisfactory detection of 95% (error rate of 5%) from the field sample, as well as a reliable volume and weight estimation with Pearson’s correlation coefficients of 0.83 and 0.84, respectively, from the described ellipsoid approach. The yield was estimated with 1.51 kg m⁻² corresponding to an average individual fruit weight of 1100 g and an average number of 1.37 pumpkins per m². Moreover, spatial distribution of aggregated fruit densities and weights were calculated to assess in-field optimization potential for agronomic management as demonstrated between a shaded edge compared to the rest of the field. The proposed approach provides the Hokkaido producer useful information for more targeted pre-harvest marketing strategies, since most food retailers request homogeneous lots within prescribed size or weight classes. Full article

(This article belongs to the Special Issue Sensors in Agriculture 2020)

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14 pages, 2424 KiB

Open AccessArticle

Assessing Effects of Salinity on the Performance of a Low-Cost Wireless Soil Water Sensor

by Srinivasa Rao Peddinti, Jan W. Hopmans, Majdi Abou Najm and Isaya Kisekka

Sensors 2020, 20(24), 7041; https://doi.org/10.3390/s20247041 - 9 Dec 2020

Cited by 17 | Viewed by 4002

Abstract

Low-cost, accurate soil water sensors combined with wireless communication in an internet of things (IoT) framework can be harnessed to enhance the benefits of precision irrigation. However, the accuracy of low-cost sensors (e.g., based on resistivity or capacitance) can be affected by many factors, including salinity, temperature, and soil structure. Recent developments in wireless sensor networks offer new possibilities for field-scale monitoring of soil water content (SWC) at high spatiotemporal scales, but to install many sensors in the network, the cost of the sensors must be low, and the mechanism of operation needs to be robust, simple, and consume low energy for the technology to be practically relevant. This study evaluated the performance of a resistivity–capacitance-based wireless sensor (Sensoterra BV, 1018LE Amsterdam, Netherlands) under different salinity levels, temperature, and soil types in a laboratory. The sensors were evaluated in glass beads, Oso Flaco sand, Columbia loam, and Yolo clay loam soils. A nonlinear relationship was exhibited between the sensor measured resistance (

Ω

) and volumetric soil water content (θ). The

Ω

–

θ

relationship differed by soil type and was affected by soil solution salinity. The sensor was extremely sensitive at higher water contents with high uncertainty, and insensitive at low soil water content accompanied by low uncertainty. The soil solution salinity effects on the

Ω

–

θ

relationship were found to be reduced from sand to sandy loam to clay loam. In clay soils, surface electrical conductivity (EC_s) of soil particles had a more dominant effect on sensor performance compared to the effect of solution electrical conductivity (EC_w). The effect of temperature on sensor performance was minimal, but sensor-to-sensor variability was substantial. The relationship between bulk electrical conductivity (EC_b) and volumetric soil water content was also characterized in this study. The results of this study reveal that if the sensor is properly calibrated, this low-cost wireless soil water sensor has the potential of improving soil water monitoring for precision irrigation and other applications at high spatiotemporal scales, due to the ease of integration into IoT frameworks. Full article

(This article belongs to the Special Issue Sensors in Agriculture 2020)

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14 pages, 3401 KiB

Open AccessArticle

Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception

by Hugo Moreno, Victor Rueda-Ayala, Angela Ribeiro, Jose Bengochea-Guevara, Juan Lopez, Gerassimos Peteinatos, Constantino Valero and Dionisio Andújar

Sensors 2020, 20(23), 6912; https://doi.org/10.3390/s20236912 - 3 Dec 2020

Cited by 11 | Viewed by 3709

Abstract

A non-destructive measuring technique was applied to test major vine geometric traits on measurements collected by a contactless sensor. Three-dimensional optical sensors have evolved over the past decade, and these advancements may be useful in improving phenomics technologies for other crops, such as woody perennials. Red, green and blue-depth (RGB-D) cameras, namely Microsoft Kinect, have a significant influence on recent computer vision and robotics research. In this experiment an adaptable mobile platform was used for the acquisition of depth images for the non-destructive assessment of branch volume (pruning weight) and related to grape yield in vineyard crops. Vineyard yield prediction provides useful insights about the anticipated yield to the winegrower, guiding strategic decisions to accomplish optimal quantity and efficiency, and supporting the winegrower with decision-making. A Kinect v2 system on-board to an on-ground electric vehicle was capable of producing precise 3D point clouds of vine rows under six different management cropping systems. The generated models demonstrated strong consistency between 3D images and vine structures from the actual physical parameters when average values were calculated. Correlations of Kinect branch volume with pruning weight (dry biomass) resulted in high coefficients of determination (R² = 0.80). In the study of vineyard yield correlations, the measured volume was found to have a good power law relationship (R² = 0.87). However due to low capability of most depth cameras to properly build 3-D shapes of small details the results for each treatment when calculated separately were not consistent. Nonetheless, Kinect v2 has a tremendous potential as a 3D sensor in agricultural applications for proximal sensing operations, benefiting from its high frame rate, low price in comparison with other depth cameras, and high robustness. Full article

(This article belongs to the Special Issue Sensors in Agriculture 2020)

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23 pages, 2712 KiB

Open AccessArticle

Potential of Hyperspectral and Thermal Proximal Sensing for Estimating Growth Performance and Yield of Soybean Exposed to Different Drip Irrigation Regimes Under Arid Conditions

by Adel H. Elmetwalli, Salah El-Hendawy, Nasser Al-Suhaibani, Majed Alotaibi, Muhammad Usman Tahir, Muhammad Mubushar, Wael M. Hassan and Salah Elsayed

Sensors 2020, 20(22), 6569; https://doi.org/10.3390/s20226569 - 17 Nov 2020

Cited by 19 | Viewed by 2880

Abstract

Proximal hyperspectral sensing tools could complement and perhaps replace destructive traditional methods for accurate estimation and monitoring of various morpho-physiological plant indicators. In this study, we assessed the potential of thermal imaging (TI) criteria and spectral reflectance indices (SRIs) to monitor different vegetative growth traits (biomass fresh weight, biomass dry weight, and canopy water mass) and seed yield (SY) of soybean exposed to 100%, 75%, and 50% of estimated crop evapotranspiration (ETc). These different plant traits were evaluated and related to TI criteria and SRIs at the beginning bloom (R1) and full seed (R6) growth stages. Results showed that all plant traits, TI criteria, and SRIs presented significant variations (p < 0.05) among irrigation regimes at both growth stages. The performance of TI criteria and SRIs for assessment of vegetative growth traits and SY fluctuated when relationships were analyzed for each irrigation regime or growth stage separately or when the data of both conditions were combined together. TI criteria and SRIs exhibited a moderate to strong relationship with vegetative growth traits when data from different irrigation regimes were pooled together at each growth stage or vice versa. The R6 and R1 growth stages are suitable for assessing SY under full (100% ETc) and severe (50% ETc) irrigation regimes, respectively, using SRIs. The overall results indicate that the usefulness of the TI and SRIs for assessment of growth, yield, and water status of soybean under arid conditions is limited to the growth stage, the irrigation level, and the combination between them. Full article

(This article belongs to the Special Issue Sensors in Agriculture 2020)

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15 pages, 5617 KiB

Open AccessArticle

A Low-Cost 3D Phenotype Measurement Method of Leafy Vegetables Using Video Recordings from Smartphones

by Zishang Yang and Yuxing Han

Sensors 2020, 20(21), 6068; https://doi.org/10.3390/s20216068 - 25 Oct 2020

Cited by 21 | Viewed by 3883

Abstract

Leafy vegetables are an essential source of the various nutrients that people need in their daily lives. The quantification of vegetable phenotypes and yield estimation are prerequisites for the selection of genetic varieties and for the improvement of planting methods. The traditional method is manual measurement, which is time-consuming and cumbersome. Therefore, there is a need for efficient and convenient in situ vegetable phenotype identification methods to provide data support for breeding research and for crop yield monitoring, thereby increasing vegetable yield. In this paper, a novel approach was developed for the in-situ determination of the three-dimensional (3D) phenotype of vegetables by recording video clips using smartphones. First, a smartphone was used to record the vegetable from different angles, and then the key frame containing the crop area in the video was obtained using an algorithm based on the vegetation index and scale-invariant feature transform algorithm (SIFT) matching. After obtaining the key frame, a dense point cloud of the vegetables was reconstructed using the Structure from Motion (SfM) method, and then the segmented point cloud and a point cloud skeleton were obtained using the clustering algorithm. Finally, the plant height, leaf number, leaf length, leaf angle, and other phenotypic parameters were obtained through the point cloud and point cloud skeleton. Comparing the obtained phenotypic parameters to the manual measurement results, the root-mean-square error (RMSE) of the plant height, leaf number, leaf length, and leaf angle were 1.82, 1.57, 2.43, and 4.7, respectively. The measurement accuracy of each indicators is greater than 80%. The results show that the proposed method provides a convenient, fast, and low-cost 3D phenotype measurement pipeline. Compared to other methods based on photogrammetry, this method does not need a labor-intensive image-capturing process and can reconstruct a high-quality point cloud model by directly recording videos of crops. Full article

(This article belongs to the Special Issue Sensors in Agriculture 2020)

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14 pages, 2312 KiB

Open AccessArticle

A New Optical Sensor Based on Laser Speckle and Chemometrics for Precision Agriculture: Application to Sunflower Plant-Breeding

by Maxime Ryckewaert, Daphné Héran, Emma Faur, Pierre George, Bruno Grèzes-Besset, Frédéric Chazallet, Yannick Abautret, Myriam Zerrad, Claude Amra and Ryad Bendoula

Sensors 2020, 20(16), 4652; https://doi.org/10.3390/s20164652 - 18 Aug 2020

Cited by 6 | Viewed by 3823

Abstract

New instruments to characterize vegetation must meet cost constraints while providing accurate information. In this paper, we study the potential of a laser speckle system as a low-cost solution for non-destructive phenotyping. The objective is to assess an original approach combining laser speckle with chemometrics to describe scattering and absorption properties of sunflower leaves, related to their chemical composition or internal structure. A laser diode system at two wavelengths 660 nm and 785 nm combined with polarization has been set up to differentiate four sunflower genotypes. REP-ASCA was used as a method to analyze parameters extracted from speckle patterns by reducing sources of measurement error. First findings have shown that measurement errors are mostly due to unwilling residual specular reflections. Moreover, results outlined that the genotype significantly impacts measurements. The variables involved in genotype dissociation are mainly related to scattering properties within the leaf. Moreover, an example of genotype classification using REP-ASCA outcomes is given and classify genotypes with an average error of about 20%. These encouraging results indicate that a laser speckle system is a promising tool to compare sunflower genotypes. Furthermore, an autonomous low-cost sensor based on this approach could be used directly in the field. Full article

(This article belongs to the Special Issue Sensors in Agriculture 2020)

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26 pages, 16119 KiB

Open AccessArticle

Mapping of Agricultural Subsurface Drainage Systems Using a Frequency-Domain Ground Penetrating Radar and Evaluating Its Performance Using a Single-Frequency Multi-Receiver Electromagnetic Induction Instrument

by Triven Koganti, Ellen Van De Vijver, Barry J. Allred, Mogens H. Greve, Jørgen Ringgaard and Bo V. Iversen

Sensors 2020, 20(14), 3922; https://doi.org/10.3390/s20143922 - 14 Jul 2020

Cited by 23 | Viewed by 7607

Abstract

Subsurface drainage systems are commonly used to remove surplus water from the soil profile of a poorly drained farmland. Traditional methods for drainage mapping involve the use of tile probes and trenching equipment that are time-consuming, labor-intensive, and invasive, thereby entailing an inherent risk of damaging the drainpipes. Effective and efficient methods are needed in order to map the buried drain lines: (1) to comprehend the processes of leaching and offsite release of nutrients and pesticides and (2) for the installation of a new set of drain lines between the old ones to enhance the soil water removal. Non-invasive geophysical soil sensors provide a potential alternative solution. Previous research has mainly showcased the use of time-domain ground penetrating radar, with variable success, depending on local soil and hydrological conditions and the central frequency of the specific equipment used. The objectives of this study were: (1) to test the use of a stepped-frequency continuous wave three-dimensional ground penetrating radar (3D-GPR) with a wide antenna array for subsurface drainage mapping and (2) to evaluate its performance with the use of a single-frequency multi-receiver electromagnetic induction (EMI) sensor in-combination. This sensor combination was evaluated on twelve different study sites with various soil types with textures ranging from sand to clay till. While the 3D-GPR showed a high success rate in finding the drainpipes at five sites (sandy, sandy loam, loamy sand, and organic topsoils), the results at the other seven sites were less successful due to the limited penetration depth of the 3D-GPR signal. The results suggest that the electrical conductivity estimates produced by the inversion of apparent electrical conductivity data measured by the EMI sensor could be a useful proxy for explaining the success achieved by the 3D-GPR in finding the drain lines. Full article

(This article belongs to the Special Issue Sensors in Agriculture 2020)

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21 pages, 5830 KiB

Open AccessArticle

A Canopy Information Measurement Method for Modern Standardized Apple Orchards Based on UAV Multimodal Information

by Guoxiang Sun, Xiaochan Wang, Haihui Yang and Xianjie Zhang

Sensors 2020, 20(10), 2985; https://doi.org/10.3390/s20102985 - 25 May 2020

Cited by 17 | Viewed by 4779

Abstract

To make canopy information measurements in modern standardized apple orchards, a method for canopy information measurements based on unmanned aerial vehicle (UAV) multimodal information is proposed. Using a modern standardized apple orchard as the study object, a visual imaging system on a quadrotor UAV was used to collect canopy images in the apple orchard, and three-dimensional (3D) point-cloud models and vegetation index images of the orchard were generated with Pix4Dmapper software. A row and column detection method based on grayscale projection in orchard index images (RCGP) is proposed. Morphological information measurements of fruit tree canopies based on 3D point-cloud models are established, and a yield prediction model for fruit trees based on the UAV multimodal information is derived. The results are as follows: (1) When the ground sampling distance (GSD) was 2.13–6.69 cm/px, the accuracy of row detection in the orchard using the RCGP method was 100.00%. (2) With RCGP, the average accuracy of column detection based on grayscale images of the normalized green (NG) index was 98.71–100.00%. The hand-measured values of H, S_XOY, and V of the fruit tree canopy were compared with those obtained with the UAV. The results showed that the coefficient of determination R² was the most significant, which was 0.94, 0.94, and 0.91, respectively, and the relative average deviation (RAD_avg) was minimal, which was 1.72%, 4.33%, and 7.90%, respectively, when the GSD was 2.13 cm/px. Yield prediction was modeled by the back-propagation artificial neural network prediction model using the color and textural characteristic values of fruit tree vegetation indices and the morphological characteristic values of point-cloud models. The R² value between the predicted yield values and the measured values was 0.83–0.88, and the RAD value was 8.05–9.76%. These results show that the UAV-based canopy information measurement method in apple orchards proposed in this study can be applied to the remote evaluation of canopy 3D morphological information and can yield information about modern standardized orchards, thereby improving the level of orchard informatization. This method is thus valuable for the production management of modern standardized orchards. Full article

(This article belongs to the Special Issue Sensors in Agriculture 2020)

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25 pages, 9754 KiB

Open AccessArticle

Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis

by Huaimin Li, Weipan Lin, Fangrong Pang, Xiaoping Jiang, Weixing Cao, Yan Zhu and Jun Ni

Sensors 2020, 20(10), 2894; https://doi.org/10.3390/s20102894 - 20 May 2020

Cited by 6 | Viewed by 3436

Abstract

An instrument developed to monitor and diagnose crop growth can quickly and non-destructively obtain crop growth information, which is helpful for crop field production and management. Focusing on the problems with existing two-band instruments used for crop growth monitoring and diagnosis, such as insufficient information available on crop growth and low accuracy of some growth indices retrieval, our research team developed a portable three-band instrument for crop-growth monitoring and diagnosis (CGMD) that obtains a larger amount of information. Based on CGMD, this paper carried out studies on monitoring wheat growth indices. According to the acquired three-band reflectance spectra, the combined indices were constructed by combining different bands, two-band vegetation indices (NDVI, RVI, and DVI), and three-band vegetation indices (TVI-1 and TVI-2). The fitting results of the vegetation indices obtained by CGMD and the commercial instrument FieldSpec HandHeld2 was high and the new instrument could be used for monitoring the canopy vegetation indices. By fitting each vegetation index to the growth index, the results showed that the optimal vegetation indices corresponding to leaf area index (LAI), leaf dry weight (LDW), leaf nitrogen content (LNC), and leaf nitrogen accumulation (LNA) were TVI-2, TVI-1, NDVI (R₇₃₀, R₈₁₅), and NDVI (R₇₃₀, R₈₁₅), respectively. R² values corresponding to LAI, LDW, LNC and LNA were 0.64, 0.84, 0.60, and 0.82, respectively, and their relative root mean square error (RRMSE) values were 0.29, 0.26, 0.17, and 0.30, respectively. The addition of the red spectral band to CGMD effectively improved the monitoring results of wheat LAI and LDW. Focusing the problem of vegetation index saturation, this paper proposed a method to construct the wheat-growth-index spectral monitoring models that were defined according to the growth periods. It improved the prediction accuracy of LAI, LDW, and LNA, with R² values of 0.79, 0.85, and 0.85, respectively, and the RRMSE values of these growth indices were 0.22, 0.23, and 0.28, respectively. The method proposed here could be used for the guidance of wheat field cultivation. Full article

(This article belongs to the Special Issue Sensors in Agriculture 2020)

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11 pages, 1274 KiB

Open AccessLetter

A Portable Spectrometric System for Quantitative Prediction of the Soluble Solids Content of Apples with a Pre-calibrated Multispectral Sensor Chipset

by Nhut-Thanh Tran and Masayuki Fukuzawa

Sensors 2020, 20(20), 5883; https://doi.org/10.3390/s20205883 - 17 Oct 2020

Cited by 22 | Viewed by 4045

Abstract

A portable spectrometric system for nondestructive assessment of the soluble solids content (SSC) of fruits for practical applications has been proposed and its performance has been examined by an experiment on quantitative prediction of the SSC of apples. Although the spectroscopic technique is a powerful tool for predicting the internal qualities of fruits, its practical applications are limited due to its high cost and complexity. In the proposed system, the spectra of apples were collected by a simple optical setup with a cheap pre-calibrated multispectral chipset. An optimal multiple linear regression model with five wavebands at 900, 760, 730, 680, and 535 nm revealed the best performance with the coefficient of determination of prediction and the root mean square error of prediction of 0.861 and 0.403 °Brix, respectively, which was comparable to that of the previous studies using dispersive spectrometers. Compared with previously reported systems using discrete filters or light emitting diodes, the proposed system was superior in terms of manufacturability and reproducibility. The experimental results confirmed that the proposed system had a considerable potential for practical, cost-effective applications of the SSC prediction, not only for apples but also for other fruits. Full article

(This article belongs to the Special Issue Sensors in Agriculture 2020)

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