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Special Issue "Sensors in Agriculture 2020"

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

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editor

Prof. Dr. Dimitrios Moshou
Website
Guest Editor
Head of Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki (A.U.Th.), P.O. 275, 54124 Thessaloniki, Greece
Interests: sensor systems for automated detection and mapping of crop enemies and threat situations (weeds, fungi, viruses, and insects); sensor systems for the detection, recognition, and mapping of nutrient stresses in crops; hyperspectral, multispectral, fluorescence, fluorescence kinetics, computer vision, thermal, lidar, and multisensor systems for crop status sensing and phenotyping; yield mapping in orchards and arable crops by using new technologies (GNSS, RTK-GPS, Zigbee, ambient computing); sensors for viticulture and wine quality; produce and activity traceability systems in the field by using new technologies (RFID, barcode, GPS, Zigbee, wearable computers, etc); bio-inspired information processing, neuroscience, self-organization, and computational intelligence; intelligent control of mechatronic systems; cyber-physical systems; industry 4.0; Internet of Things, and M2M systems; information and data fusion; cognitive robotics and active learning systems, sensor-based environment awareness
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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

Manuscript Submission Information

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

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 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.

Published Papers (2 papers)

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Research

Open AccessArticle
A Canopy Information Measurement Method for Modern Standardized Apple Orchards Based on UAV Multimodal Information
Sensors 2020, 20(10), 2985; https://doi.org/10.3390/s20102985 - 25 May 2020
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 [...] Read more.
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, SXOY, 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 (RADavg) 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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Open AccessArticle
Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis
Sensors 2020, 20(10), 2894; https://doi.org/10.3390/s20102894 - 20 May 2020
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 [...] Read more.
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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