Special Issue "Sensing and Automated Systems for Improved Crop Management"

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Innovative Cropping Systems".

Deadline for manuscript submissions: closed (31 August 2018).

Special Issue Editor

Dr. Lav R. Khot
E-Mail Website
Guest Editor
Department of Biological Systems Engineering, CPAAS/IAREC, Washington State University, Prosser, WA, USA
Interests: remote sensing (unmanned and manned aerial systems); ground-based (proximal) crop sensing; decision support systems and information delivery technologies; precise applications of various production inputs; agricultural machinery and processes; data-based modeling
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Special Issue Information

Dear Colleagues,

The crop production management technology landscape is changing rapidly, with advances in optical and volatile sensing techniques and in versatile ground- and aerial-platforms. Sensing tools and platforms are also being integrated through implementation of the Internet of things (IOT) concepts. Such techniques, if tested and vetted effectively by the scientific community, can, and will, ease logical concerns about the time and labor required for gathering actionable near-real-time field/plant level intelligence for effective crop (loss) management. This Special Issue of Agronomy is focused on the publication of state-of-the-art techniques/methods in sensing and automated systems for improved crop monitoring and management. Efforts will be made to publish articles that emphasize advances in approaches with cropping system-specific case studies.

Prof. Dr. Lav Khot
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. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

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

  • Precision agriculture
  • Proximal and remote sensing
  • Optical sensors
  • Volatile sensors
  • Data-to-decision support tools
  • Crop loss management

Published Papers (8 papers)

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Research

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Article
Remote Detection of Growth Dynamics in Red Lettuce Using a Novel Chlorophyll a Fluorometer
Agronomy 2018, 8(10), 227; https://doi.org/10.3390/agronomy8100227 - 16 Oct 2018
Cited by 2 | Viewed by 2164
Abstract
The production of food crops in controlled environment agriculture (CEA) can help mitigate food insecurity that may result from increasingly frequent and severe weather events in agricultural areas. Lighting is an absolute requirement for crop growth in CEA, and is undergoing rapid advances [...] Read more.
The production of food crops in controlled environment agriculture (CEA) can help mitigate food insecurity that may result from increasingly frequent and severe weather events in agricultural areas. Lighting is an absolute requirement for crop growth in CEA, and is undergoing rapid advances with the advent of tunable, light emitting diode (LED) systems. The integration of these systems into existing CEA environmental control architectures is in its infancy and would benefit from a non-invasive, rapid, real-time, remote sensor that could track crop growth under different lighting regimes. A newly-developed remote chlorophyll a fluorescence (ChlF) sensing device is described herein that provides direct, remote, real-time physiological data collection for integration into tunable LED lighting control systems, thereby enabling better control of crop growth and energy efficiency. Data collected by this device can be used to accurately model growth of red lettuce plants. In addition to monitoring growth, this system can predict relative growth rates (RGR), net assimilation rates (NAR), plant area (PA), and leaf area ratio (LAR). Full article
(This article belongs to the Special Issue Sensing and Automated Systems for Improved Crop Management)
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Article
Evaluating Two Crop Circle Active Canopy Sensors for In-Season Diagnosis of Winter Wheat Nitrogen Status
Agronomy 2018, 8(10), 201; https://doi.org/10.3390/agronomy8100201 - 21 Sep 2018
Cited by 14 | Viewed by 1983
Abstract
Active crop canopy sensors can be used for non-destructive real-time diagnosis of crop nitrogen (N) status and guiding in-season N management. However, limited studies have compared the performances of two commercially available sensors with three different wavebands: Crop Circle ACS-470 (CC-470) and Crop [...] Read more.
Active crop canopy sensors can be used for non-destructive real-time diagnosis of crop nitrogen (N) status and guiding in-season N management. However, limited studies have compared the performances of two commercially available sensors with three different wavebands: Crop Circle ACS-470 (CC-470) and Crop Circle ACS-430 (CC-430). The objective of this study was to evaluate the performances of CC-470 and CC-430 sensors for estimating winter wheat (Triticum aestivum L.) N status at different measurement heights (40 cm, 70 cm and 100 cm) and growth stages. Results indicated that the canopy reflectance values of CC-470 were more affected by height compared to the CC-430 sensor. The normalized difference red edge (NDRE) and red edge chlorophyll index (CIRE) of CC-430 were stable at the three different measuring heights. The relationships between these indices and the N status indicators were stronger at the Feekes 9–10 stages than the Feekes 6–7 stages for both sensors; however, the CC-430 sensor-based vegetation indices had higher coefficient of determination (R2) values for both stages. It is concluded that the CC-430 sensor is more reliable than CC-470 for winter wheat N status estimation due to its capability of making height-independent measurements. These results demonstrated the importance of considering the influences of height when using active canopy sensors in field measurements. Full article
(This article belongs to the Special Issue Sensing and Automated Systems for Improved Crop Management)
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Article
Effect of Unmanned Aerial Vehicle Flight Height on Droplet Distribution, Drift and Control of Cotton Aphids and Spider Mites
Agronomy 2018, 8(9), 187; https://doi.org/10.3390/agronomy8090187 - 13 Sep 2018
Cited by 21 | Viewed by 2024
Abstract
Unmanned aerial vehicles (UAVs), as emerging plant protection machinery, have the advantages of high operational efficiency, high speed, and low drift. The current study aimed to elucidate the characteristics of droplet distribution and drift, control efficiency on cotton aphids and spider mites, and [...] Read more.
Unmanned aerial vehicles (UAVs), as emerging plant protection machinery, have the advantages of high operational efficiency, high speed, and low drift. The current study aimed to elucidate the characteristics of droplet distribution and drift, control efficiency on cotton aphids and spider mites, and attachment and absorption of cotton leaves during UAV spraying. Kromekote card and filter paper are used as samplers to collect droplets, and the droplet density, coverage rate, deposition, and drift percentage are statistically analyzed. The pooled results showed that the droplet uniformity, the droplet coverage rate, the deposition, and the drifting ability are higher when the UAV flight height was 2 m. The control effects by UAV spraying on cotton aphids and spider mites were 63.7% and 61.3%, respectively. These values are slightly inferior to those obtained through boom spraying. Cotton leaf attachment and absorption of spirodiclofen after UAV spraying were slightly lower than those after boom spraying, therefore, the control efficiency of cotton pests is slightly different. According to the different flight height operations by the UAV sprayer, the drift capability of the droplets at 2 m flight height was large, and the droplet uniformity and deposition were satisfactory. The research results could provide the theoretical basis and technical support for UAV operation. Full article
(This article belongs to the Special Issue Sensing and Automated Systems for Improved Crop Management)
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Article
Effects of Dosage and Spraying Volume on Cotton Defoliants Efficacy: A Case Study Based on Application of Unmanned Aerial Vehicles
Agronomy 2018, 8(6), 85; https://doi.org/10.3390/agronomy8060085 - 30 May 2018
Cited by 15 | Viewed by 2174
Abstract
Plant protection unmanned aerial vehicles (UAVs) consist of light and small UAVs with pesticide spraying equipment. The advantage of UAVs is using low-volume spray technology to replace the traditional large-volume mass locomotive spray technology. Defoliant spraying is a key link in the mechanized [...] Read more.
Plant protection unmanned aerial vehicles (UAVs) consist of light and small UAVs with pesticide spraying equipment. The advantage of UAVs is using low-volume spray technology to replace the traditional large-volume mass locomotive spray technology. Defoliant spraying is a key link in the mechanized cotton harvest, as sufficient and uniform spraying can improve the defoliation quality and decrease the cotton trash content. However, cotton is planted at high density in Xinjiang, with leaves in two adjacent rows seriously overlapped, making the lower leaves poorly sprayed. Thus, the defoliation effect is poor, and the cotton quality is degraded. To improve the effect of defoliation and reduce the losses caused by boom sprayer rolling, the effect of defoliant dosage on defoliation, boll opening, absorption and decontamination in cotton leaves and the effect of spraying volume on absorption and decontamination in cotton leaves sprayed by UAVs are studied. The pooled results indicate that plant protection UAVs could be used for cotton defoliants spraying with a twice defoliant spraying strategy, and the defoliant dosage has no significant effect on seed cotton yield and fiber quality in Xinjiang. The residue of thidiazuron in cotton leaves reaches the maximum at four days after spraying, the residue of diuron in cotton leaves reaches the maximum at one day after second spraying. The thidiazuron and diuron residues are increased with spraying volume at rang of 17.6–29.0 L/ha. When the spraying volume is less than 17.6 L/ha, the residue of thidiazuron and diuron is reduced. The research results could provide a reference for further optimization of the spraying parameters of cotton defoliant by plant protection UAVs. Full article
(This article belongs to the Special Issue Sensing and Automated Systems for Improved Crop Management)
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Article
High-Throughput Phenotyping of Seed/Seedling Evaluation Using Digital Image Analysis
Agronomy 2018, 8(5), 63; https://doi.org/10.3390/agronomy8050063 - 03 May 2018
Cited by 14 | Viewed by 3246
Abstract
Image-based evaluation of phenotypic traits has been applied for plant architecture, seed, canopy growth/vigor, and root characterization. However, such applications using computer vision have not been exploited for the purpose of assessing the coleoptile length and herbicide injury in seeds. In this study, [...] Read more.
Image-based evaluation of phenotypic traits has been applied for plant architecture, seed, canopy growth/vigor, and root characterization. However, such applications using computer vision have not been exploited for the purpose of assessing the coleoptile length and herbicide injury in seeds. In this study, high-throughput phenotyping using digital image analysis was applied to evaluate seed/seedling traits. Images of seeds or seedlings were acquired using a commercial digital camera and analyzed using custom-developed image processing algorithms. Results from two case studies demonstrated that it was possible to use image-based high-throughput phenotyping to assess seeds/seedlings. In the seedling evaluation study, using a color-based detection method, image-based and manual coleoptile length were positively and significantly correlated (p < 0.0001) with reasonable accuracy (r = 0.69–0.91). As well, while using a width-and-color-based detection method, the correlation coefficient was also significant (p < 0.0001, r = 0.89). The improvement of the germination protocol designed for imaging will increase the throughput and accuracy of coleoptile detection using image processing methods. In the herbicide study, using image-based features, differences between injured and uninjured seedlings can be detected. In the presence of the treatment differences, such a technique can be applied for non-biased symptom rating. Full article
(This article belongs to the Special Issue Sensing and Automated Systems for Improved Crop Management)
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Article
Characterizing Spatial Variability in Soil Water Content for Precision Irrigation Management
Agronomy 2018, 8(5), 59; https://doi.org/10.3390/agronomy8050059 - 24 Apr 2018
Cited by 8 | Viewed by 1626
Abstract
Among one of the many challenges in implementing precision irrigation is to obtain an accurate characterization of the soil water content (SWC) across spatially variable fields along the crop growing season. The accuracy of characterizing SWC has been tested primarily on a small-scale [...] Read more.
Among one of the many challenges in implementing precision irrigation is to obtain an accurate characterization of the soil water content (SWC) across spatially variable fields along the crop growing season. The accuracy of characterizing SWC has been tested primarily on a small-scale and has received little attention from the scientific community at the field scale. Hence, the objective of this study was to assess the characterization of the spatial distribution of soil water content at the field scale by the apparent electrical conductivity (ECa). In evaluating the current aim, ECa survey was compared against repeated measurements of SWC at five depths using neutron probe. Results showed that mean SWC was different across ECa derived management zones, which indicates that on a macro-scale, soil ECa could effectively characterize the mean differences in SWC across management zones. Results also showed that deep ECa (0–150 cm) survey outperformed shallow survey (0–75 cm). Considering other soil properties, such as organic matter content and salt content, further improved the relationship between SWC and ECa. Full article
(This article belongs to the Special Issue Sensing and Automated Systems for Improved Crop Management)
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Article
Assessing Olive Evapotranspiration Partitioning from Soil Water Balance and Radiometric Soil and Canopy Temperatures
Agronomy 2018, 8(4), 43; https://doi.org/10.3390/agronomy8040043 - 06 Apr 2018
Cited by 4 | Viewed by 1934
Abstract
Evapotranspiration (ETc) partitioning and obtaining of FAO56 dual crop coefficient (Kc) for olive was carried out with the SIMDualKc software application for root zone and topsoil soil water balance based on the dual crop coefficients. A simplified [...] Read more.
Evapotranspiration (ETc) partitioning and obtaining of FAO56 dual crop coefficient (Kc) for olive was carried out with the SIMDualKc software application for root zone and topsoil soil water balance based on the dual crop coefficients. A simplified two source-energy balance model (STSEB), based on daily remotely sensed soil and canopy thermal infrared data and retrieval of surface fluxes, also provided information on partitioning ETc for the olive orchard. Both models were calibrated and validated with ground-based, sap flow-derived transpiration rates, and their performance was compared in partitioning ETc for incomplete cover, intensive olive grown in orchards (≤300 trees ha−1). The SIMDualKc proved adequate in partitioning ETc. The STSEB model underestimated ETc mostly by inadequately simulating soil evaporation and its contribution to the total latent heat flux. Such results suggest difficulties in using information from the STSEB algorithm for assessing ETc and dual Kc crop coefficients of intensive olive orchards with incomplete ground cover. Full article
(This article belongs to the Special Issue Sensing and Automated Systems for Improved Crop Management)
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Review

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Review
Sensing and Automation in Pruning of Apple Trees: A Review
Agronomy 2018, 8(10), 211; https://doi.org/10.3390/agronomy8100211 - 30 Sep 2018
Cited by 26 | Viewed by 2865
Abstract
Pruning is one of the most important tree fruit production activities, which is highly dependent on human labor. Skilled labor is in short supply, and the increasing cost of labor is becoming a big issue for the tree fruit industry. Meanwhile, worker safety [...] Read more.
Pruning is one of the most important tree fruit production activities, which is highly dependent on human labor. Skilled labor is in short supply, and the increasing cost of labor is becoming a big issue for the tree fruit industry. Meanwhile, worker safety is another issue in the manual pruning. Growers are motivated to seek mechanical or robotic solutions for reducing the amount of hand labor required for pruning. Identifying tree branches/canopies with sensors as well as automated operating pruning activity are the important components in the automated pruning system. This paper reviews the research and development of sensing and automated systems for branch pruning in apple production. Tree training systems, pruning strategies, 3D structure reconstruction of tree branches, and practice mechanisms or robotics are some of the developments that need to be addressed for an effective tree branch pruning system. Our study summarizes the potential opportunities for automatic pruning with machine-friendly modern tree architectures, previous studies on sensor development, and efforts to develop and deploy mechanical/robotic systems for automated branch pruning. We also describe two examples of qualified pruning strategies that could potentially simplify the automated pruning decision and pruning end-effector design. Finally, the limitations of current pruning technologies and other challenges for automated branch pruning are described, and possible solutions are discussed. Full article
(This article belongs to the Special Issue Sensing and Automated Systems for Improved Crop Management)
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