Special Issue "Precision Agriculture"

A special issue of Agronomy (ISSN 2073-4395).

Deadline for manuscript submissions: closed (30 April 2020).

Special Issue Editors

Prof. Spyros Fountas
Website
Guest Editor
Laboratory of Agricultural Machinery, Department of Natural Resources Management and Agricultural Engineering, School of Agricultural Production, Engineering and Environment, Agricultural University of Athens, 75 Iera Odos Str., Botanikos 11855, Athens, Greece
Interests: remote sensing; GIS; information management; precision farming
Special Issues and Collections in MDPI journals
Dr. Frits van Evert
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Guest Editor
Wageningen University & Research, Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands
Interests: Precision agriculture, weed science, sustainability, potatoes, nitrogen, robotics, crop growth models, information systems
Dr. Thanos Balafoutis
Website
Guest Editor
Agricultural University of Athens, Laboratory of Agricultural Engineering, Iera Odos 75, Athens 11855, Greece
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Precision agriculture is a management strategy that focuses on monitoring, measurement, and responses to inter- and intra-variability in cropping systems. Precision agriculture is based on the rapid deployment of sensing technologies, management information systems, and variable rate technologies with appropriate agronomic and economic models. The benefits of using precision agriculture solutions include the optimization of process inputs, production cost reduction, and potentially increasing crop yields and quality, while reducing the environmental impact.

This Special Issue aims to discuss various aspects of precision agriculture applications, technologies, and management methods. This will include the state-of-the-art on technologies applied in different cropping systems, agronomic models to interpret precision agriculture measured data, economic implications and adoption of precision agriculture technologies, and precision agriculture applications. The latter includes areas in all agricultural activities, such as precision irrigation, precision fertilization, and selective harvesting. Studies on applications regarding all cropping systems are invited to be submitted.

We invite you to contribute to this Issue by submitting comprehensive reviews, case studies, or research articles that focus on scientific methods, technological tools, and innovative statistical analyses, in order to provide an opportunity for learning the state-of-the-art and for discussion on future directions in precision agriculture. Papers selected for this Special Issue are subject to a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, developments, and applications.

Dr. Spyros Fountas
Dr. Frits van Evert
Dr. Thanos Balafoutis
Guest Editors

Manuscript Submission Information

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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 1600 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
  • applications, technologies and management methods
  • all cropping systems

Published Papers (14 papers)

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Open AccessArticle
Smart Farming Technology Trends: Economic and Environmental Effects, Labor Impact, and Adoption Readiness
Agronomy 2020, 10(5), 743; https://doi.org/10.3390/agronomy10050743 - 21 May 2020
Abstract
Farming faces challenges that increase the adverse effects on farms’ economics, labor, and the environment. Smart farming technologies (SFTs) are expected to assist in reverting this situation. In this work, 1064 SFTs were derived from scientific papers, research projects, and industrial products. They [...] Read more.
Farming faces challenges that increase the adverse effects on farms’ economics, labor, and the environment. Smart farming technologies (SFTs) are expected to assist in reverting this situation. In this work, 1064 SFTs were derived from scientific papers, research projects, and industrial products. They were classified by technology readiness level (TRL), typology, and field operation, and they were assessed for their economic, environmental, and labor impact, as well as their adoption readiness from end-users. It was shown that scientific articles dealt with SFTs of lower TRL than research projects. In scientific articles, researchers investigated mostly recording technologies, while, in research projects, they focused primarily on farm management information systems and robotic/automation systems. Scouting technologies were the main SFT type in scientific papers and research projects, but variable rate application technologies were mostly located in commercial products. In scientific papers, there was limited analysis of economic, environmental, and labor impact of the SFTs under investigation, while, in research projects, these impacts were studied thoroughly. Further, in commercial SFTs, the focus was on economic impact and less on labor and environmental issues. With respect to adoption readiness, it was found that all of the factors to facilitate SFT adoption became more positive moving from SFTs in scientific papers to fully functional commercial SFTs, indicating that SFTs reach the market when most of these factors are addressed for the benefit of the farmers. This SFT analysis is expected to inform researchers on adapting their research, as well as help policy-makers adjust their strategy toward digitized agriculture adoption and farmers with the current situation and future trends of SFTs. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle
Main Motivational Factors of Farmers Adopting Precision Farming in Hungary
Agronomy 2020, 10(4), 610; https://doi.org/10.3390/agronomy10040610 - 24 Apr 2020
Abstract
The basic question of our research is what crop-producing farmers know about PF (precision farming), and how economic value and social factors motivate the acceptance and implementation of PF. We conducted a cross-sectional survey, using standardized questionnaires, in 2018, that was nationally representative [...] Read more.
The basic question of our research is what crop-producing farmers know about PF (precision farming), and how economic value and social factors motivate the acceptance and implementation of PF. We conducted a cross-sectional survey, using standardized questionnaires, in 2018, that was nationally representative of Hungarian crop producers. Besides this, we conducted 30 semi-structured interviews about the meaning of PF, with the farmers who use PF in practice. They defined it as a tool of strategic planning, to serve input savings, using state-of-the-art technologies. Based on the questionnaire, we found that the farmers currently applying PF do not seem to have such a significant impact on the agricultural society that would make others want to move to precision technology, following their example. As a result of the factor analysis, we could differentiate direct and indirect factors. Potential human resources are undereducated, their willingness to improve their knowledge is low, and the level of cooperation ability is low, making it excessively difficult, or even impossible, to acquire the equipment necessary for a technology switch and to purchase the necessary services. It can be concluded that age, production, and technical usefulness carries greater weight over things like monetary factors, productivity of cultivated land, knowledge capital, and willingness of Hungarian farmers to cooperate. Full article
(This article belongs to the Special Issue Precision Agriculture)
Open AccessArticle
Identification of Cabbage Seedling Defects in a Fast Automatic Transplanter Based on the maxIOU Algorithm
Agronomy 2020, 10(1), 65; https://doi.org/10.3390/agronomy10010065 - 02 Jan 2020
Abstract
The automatic identification of seedling defects is an important technology of an intelligent automatic transplanting machine, which effectively improves the quality of the transplanting machine’s operation. The accurate segmentation of seedling substrate and seedling region is the key to the success of the [...] Read more.
The automatic identification of seedling defects is an important technology of an intelligent automatic transplanting machine, which effectively improves the quality of the transplanting machine’s operation. The accurate segmentation of seedling substrate and seedling region is the key to the success of the seedling defect recognition algorithm. This paper proposes the maxIOU algorithm to calculate the image segmentation threshold: The image G channel and excess green color space were selected as the color space for the segmentation of the substrate region and seedling region by analyzing the color histogram. Several images were randomly selected from the dataset to generate a training set and were labeled manually as the ground truth. The training set images were segmented using a threshold of zero to 255, and the intersection over union (IOU) were calculated using the algorithm segmented result and the ground truth. The threshold corresponding to the average IOU maximum was used as the segmentation threshold. After image segmentation, three features (area of the substrate, area of the seedling, and filling ratio of the lower part of the substrate) were obtained by the algorithm, and the image was identified for whether there was an empty conveyor belt, seedling deficiency, multiple seedlings, skew, and damaged substrate. The algorithm was tested on the automatic transplanter test platform. The experiment results were as follows: Firstly, the image segmentation threshold was calculated by the maxIOU method. The color component interval corresponding to the segmented substrate region was [0, 24] in the G channel, and the color component interval corresponding to the segmented seedling region was [21, 255] in the excess green channel. The average IOU of the substrate area was 0.854, and the average IOU of the seedling area was 0.820 in the verification experiment. Secondly, a dataset including 431 normal seedling images and 69 defective seedling images (empty conveyor belt, seedling deficiency, multiple seedlings, skew, and damaged substrate) was identified for defects. The accuracy, precision, and recall were 97.6%, 97.4%, and 99.8%. The processing time was 71.4 ms. The conclusion of the experiment was as follows: the maxIOU algorithm had high accuracy in the segmentation of the substrate and seedling region. The defect identification algorithm had high accuracy for defect identification of cabbage seedlings, and the algorithm had good real-time performance, which can be applied to high speed field transplanters. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle
Estimating Canopy Parameters Based on the Stem Position in Apple Trees Using a 2D LiDAR
Agronomy 2019, 9(11), 740; https://doi.org/10.3390/agronomy9110740 - 11 Nov 2019
Cited by 1
Abstract
Data of canopy morphology are crucial for cultivation tasks within orchards. In this study, a 2D light detection and range (LiDAR) laser scanner system was mounted on a tractor, tested on a box with known dimensions (1.81 m × 0.6 m × 0.6 [...] Read more.
Data of canopy morphology are crucial for cultivation tasks within orchards. In this study, a 2D light detection and range (LiDAR) laser scanner system was mounted on a tractor, tested on a box with known dimensions (1.81 m × 0.6 m × 0.6 m), and applied in an apple orchard to obtain the 3D structural parameters of the trees (n = 224). The analysis of a metal box which considered the height of four sides resulted in a mean absolute error (MAE) of 8.18 mm with a bias (MBE) of 2.75 mm, representing a root mean square error (RMSE) of 1.63% due to gaps in the point cloud and increased incident angle with enhanced distance between laser aperture and the object. A methodology based on a bivariate point density histogram is proposed to estimate the stem position of each tree. The cylindrical boundary was projected around the estimated stem positions to segment each individual tree. Subsequently, height, stem diameter, and volume of the segmented tree point clouds were estimated and compared with manual measurements. The estimated stem position of each tree was defined using a real time kinematic global navigation satellite system, (RTK-GNSS) resulting in an MAE and MBE of 33.7 mm and 36.5 mm, respectively. The coefficient of determination (R2) considering manual measurements and estimated data from the segmented point clouds appeared high with, respectively, R2 and RMSE of 0.87 and 5.71% for height, 0.88 and 2.23% for stem diameter, as well as 0.77 and 4.64% for canopy volume. Since a certain error for the height and volume measured manually can be assumed, the LiDAR approach provides an alternative to manual readings with the advantage of getting tree individual data of the entire orchard. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle
Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model
Agronomy 2019, 9(11), 719; https://doi.org/10.3390/agronomy9110719 - 06 Nov 2019
Abstract
Integrating remote sensing data into crop models offers opportunities for improved crop yield estimation. To compare site-specific yield estimation accuracy of a stand-alone crop model with a data-integration approach, a study was conducted in 2016–2017 with nitrogen (N)-fertilized and unfertilized treatments across a [...] Read more.
Integrating remote sensing data into crop models offers opportunities for improved crop yield estimation. To compare site-specific yield estimation accuracy of a stand-alone crop model with a data-integration approach, a study was conducted in 2016–2017 with nitrogen (N)-fertilized and unfertilized treatments across a heterogeneous 7-ha maize field. For each treatment, yield data were grouped into five classes resulting in 109 spatial zones. In each zone, the Crop Environment Resource Synthesis (CERES)-Maize model was run using the GeoSim plugin within Quantum GIS. In the data integration approach, maize biomass values estimated using satellite imagery at the five (V5) and ten (V10) leaf collar stages were used to optimize the total soil nitrogen concentration (SLNI) and soil fertility factor (SLPF) in CERES-Maize. Without integration, maize yield was simulated with root mean square error (RMSE) of 1264 kg ha−1. Optimization of SLNI improved yield simulations at both V5 and V10. However, better simulations were obtained from optimization at V10 (RMSE 1026 kg ha−1) as compared to V5 (RMSE 1158 kg ha−1). Optimization of SLPF together with SLNI did not further improve the yield simulations. This study shows that integrating remote sensing data into a crop model can improve site-specific maize yield estimations as compared to the stand-alone crop modeling approach. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle
Nondestructive and Continuous Fresh Weight Measurements of Bell Peppers Grown in Soilless Culture Systems
Agronomy 2019, 9(10), 652; https://doi.org/10.3390/agronomy9100652 - 18 Oct 2019
Abstract
Fresh weight is a direct index of crop growth. It is difficult to continuously measure the fresh weight of bell peppers grown in soilless cultures, however, due to the difficulty in identifying the moisture condition of crops and growing media. The objective of [...] Read more.
Fresh weight is a direct index of crop growth. It is difficult to continuously measure the fresh weight of bell peppers grown in soilless cultures, however, due to the difficulty in identifying the moisture condition of crops and growing media. The objective of this study was to develop a continuous and nondestructive measuring system for the fresh weight of bell peppers grown in soilless cultures considering the moisture content of growing media. The system simultaneously measures the trellis string’s supported weight and gravitational weight using tensile load cells. The moisture weight of growing media was calibrated during the growth period using changes in moisture content before and after the first irrigation of the day. The most stable time period for the measurement, from 03:00 to 06:00, was determined by analyzing the diurnal change in relative water content. To verify the accuracy of the system, the fruits, stems, leaves, and roots’ fresh weights were measured manually. The fresh weights measured by the developed system were in good agreement with those manually measured. The results confirm that our system can reliably and accurately measure fresh weights of bell peppers grown in soilless cultures. This method can be applied to continuous growth data collection for other crops grown in soilless cultures. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle
In-Season Diagnosis of Winter Wheat Nitrogen Status in Smallholder Farmer Fields Across a Village Using Unmanned Aerial Vehicle-Based Remote Sensing
Agronomy 2019, 9(10), 619; https://doi.org/10.3390/agronomy9100619 - 09 Oct 2019
Cited by 2
Abstract
Improving nitrogen (N) management of small-scale farming systems in developing countries is crucially important for food security and sustainable development of world agriculture, but it is also very challenging. The N Nutrition Index (NNI) is a reliable indicator for crop N status, and [...] Read more.
Improving nitrogen (N) management of small-scale farming systems in developing countries is crucially important for food security and sustainable development of world agriculture, but it is also very challenging. The N Nutrition Index (NNI) is a reliable indicator for crop N status, and there is an urgent need to develop an effective method to non-destructively estimate crop NNI in different smallholder farmer fields to guide in-season N management. The eBee fixed-wing unmanned aerial vehicle (UAV)-based remote sensing system, a ready-to-deploy aircraft with a Parrot Sequoia+ multispectral camera onboard, has been used for applications in precision agriculture. The objectives of this study were to (i) determine the potential of using fixed-wing UAV-based multispectral remote sensing for non-destructive estimation of winter wheat NNI in different smallholder farmer fields across the study village in the North China Plain (NCP) and (ii) develop a practical strategy for village-scale winter wheat N status diagnosis in small scale farming systems. Four plot experiments were conducted within farmer fields in 2016 and 2017 in a village of Laoling County, Shandong Province in the NCP for evaluation of a published critical N dilution curve and for serving as reference plots. UAV remote sensing images were collected from all the fields across the village in 2017 and 2018. About 150 plant samples were collected from farmer fields and plot experiments each year for ground truthing. Two indirect and two direct approaches were evaluated for estimating NNI using vegetation indices (VIs). To facilitate practical applications, the performance of three commonly used normalized difference VIs were compared with the top performing VIs selected from 59 tested indices. The most practical and stable method was using VIs to calculate N sufficiency index (NSI) and then to estimate NNI non-destructively (R2 = 0.53–0.56). Using NSI thresholds to diagnose N status directly was quite stable, with a 57–59% diagnostic accuracy rate. This strategy is practical and least affected by the choice of VIs across fields, varieties, and years. This study demonstrates that fixed-wing UAV–based remote sensing is a promising technology for in-season diagnosis of winter wheat N status in smallholder farmer fields at village scale. The considerable variability in local soil conditions and crop management practices influenced the overall accuracy of N diagnosis, so more studies are needed to further validate and optimize the reported strategy and consecutively develop practical UAV remote sensing–based in-season N recommendation methods. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle
Evaluation of Mobile Heat Treatment System for Treating In-Field HLB-Affected Trees by Analyzing Survival Rate of Surrogate Bacteria
Agronomy 2019, 9(9), 540; https://doi.org/10.3390/agronomy9090540 - 12 Sep 2019
Cited by 1
Abstract
Huanglongbing (HLB or citrus greening) is a disease caused by an insect-transmitted bacterial pathogen Candidatus Liberibacter asiaticus (CLas). Thermotherapy has been successfully used by others to reduce the population of CLas bacteria in HLB-affected citrus trees under greenhouse studies. Thermotherapy is [...] Read more.
Huanglongbing (HLB or citrus greening) is a disease caused by an insect-transmitted bacterial pathogen Candidatus Liberibacter asiaticus (CLas). Thermotherapy has been successfully used by others to reduce the population of CLas bacteria in HLB-affected citrus trees under greenhouse studies. Thermotherapy is the application of heat as a strategy to reduce the adverse economic impact of certain pests and diseases. CLas is a fastidious, non-cultivable organism. The high variance in CLas titers in canopy samples together with this lack of cultivability makes it impossible to use classical bacteriological techniques to measure the viability either before or after treatments. Therefore, we used the survival rates of a surrogate bacterium, Klebsiella oxytoca, in order to evaluate the effectiveness of a mobile thermotherapy delivery system developed for in-field treatment of HLB-affected trees. K. oxytoca is a Gram-negative, rod-shaped bacterium that was originally isolated from soil and has been used in the development of industrial applications related to ethanol fuel production. It served as a biologically-based sensor of temperature stress (biosensor) in this study. Thermocouples and biosensor packets (plastic cups with suspended small snap-top tubes) containing the K. oxytoca were attached to an HLB-affected citrus tree and their canopy locations mapped. The mobile thermotherapy treatment hood covered the canopy of the HLB-affected tree. Then, steam and hot water were injected through nozzles inside of the hood to increase the temperature of the tree canopy. A standard temperature–time combination of 54 °C for 90 s was chosen based on preliminary studies where heat treatment parameters caused a significant reduction in CLas populations without inflicting permanent damage to the tree. The survival ratio of the K. oxytoca in the biosensor packets was found to range from complete elimination to 5% with treatments of 250 s and a maximum temperature of 54 °C. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle
Evaluation of a Ground Penetrating Radar to Map the Root Architecture of HLB-Infected Citrus Trees
Agronomy 2019, 9(7), 354; https://doi.org/10.3390/agronomy9070354 - 03 Jul 2019
Cited by 3
Abstract
This paper investigates the influences of several limiting factors on the performance of ground penetrating radar (GPR) in accurately detecting huanglongbing (HLB)-infected citrus roots and determining their main structural characteristics. First, single-factor experiments were conducted to evaluate GPR performance. The factors that were [...] Read more.
This paper investigates the influences of several limiting factors on the performance of ground penetrating radar (GPR) in accurately detecting huanglongbing (HLB)-infected citrus roots and determining their main structural characteristics. First, single-factor experiments were conducted to evaluate GPR performance. The factors that were evaluated were (i) root diameter; (ii) root moisture level; (iii) root depth; (iv) root spacing; (v) survey angle; and, (vi) soil moisture level. Second, two multi-factor field experiments were conducted to evaluate the performance of the GPR in complex orchard environments. The GPR generated a hyperbola in the radar profile upon root detection; the diameter of the root was successfully determined according to the width of the hyperbola when the roots were larger than 6 mm in diameter. The GPR also distinguished live from dead roots, a capability that is indispensable for studying the effects of soil-borne and other diseases on the citrus tree root system. The GPR can distinguish the roots only if their horizontal distance is greater than 10 cm and their vertical distance is greater than 5 cm if two or more roots are in proximity. GPR technology can be applied to determine the efficacy of advanced crop production strategies, especially under the pressures of disease and environmental stresses. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle
Mapping the Depth-to-Soil pH Constraint, and the Relationship with Cotton and Grain Yield at the Within-Field Scale
Agronomy 2019, 9(5), 251; https://doi.org/10.3390/agronomy9050251 - 21 May 2019
Cited by 3
Abstract
Subsoil alkalinity is a common issue in the alluvial cotton-growing valleys of northern New South Wales (NSW), Australia. Soil alkalinity can cause nutrient deficiencies and toxic effects, and inhibit rooting depth, which can have a detrimental impact on crop production. The depth at [...] Read more.
Subsoil alkalinity is a common issue in the alluvial cotton-growing valleys of northern New South Wales (NSW), Australia. Soil alkalinity can cause nutrient deficiencies and toxic effects, and inhibit rooting depth, which can have a detrimental impact on crop production. The depth at which a soil constraint is reached is important information for land managers, but it is difficult to measure or predict spatially. This study predicted the depth in which a pH (H2O) constraint (>9) was reached to a 1-cm vertical resolution to a 100-cm depth, on a 1070-hectare dryland cropping farm. Equal-area quadratic smoothing splines were used to resample vertical soil profile data, and a random forest (RF) model was used to produce the depth-to-soil pH constraint map. The RF model was accurate, with a Lin’s Concordance Correlation Coefficient (LCCC) of 0.63–0.66, and a Root Mean Square Error (RMSE) of 0.47–0.51 when testing with leave-one-site-out cross-validation. Approximately 77% of the farm was found to be constrained by a strongly alkaline pH greater than 9 (H2O) somewhere within the top 100 cm of the soil profile. The relationship between the predicted depth-to-soil pH constraint map and cotton and grain (wheat, canola, and chickpea) yield monitor data was analyzed for individual fields. Results showed that yield increased when a soil pH constraint was deeper in the profile, with a good relationship for wheat, canola, and chickpea, and a weaker relationship for cotton. The overall results from this study suggest that the modelling approach is valuable in identifying the depth-to-soil pH constraint, and could be adopted for other important subsoil constraints, such as sodicity. The outputs are also a promising opportunity to understand crop yield variability, which could lead to improvements in management practices. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle
Assessment of the Cutting Performance of a Robot Mower Using Custom Built Software
Agronomy 2019, 9(5), 230; https://doi.org/10.3390/agronomy9050230 - 06 May 2019
Cited by 3
Abstract
Before the introduction of positioning technologies in agriculture practices such as global navigation satellite systems (GNSS), data collection and management were time-consuming and labor-intensive tasks. Today, due to the introduction of advanced technologies, precise information on the performance of agricultural machines, and smaller [...] Read more.
Before the introduction of positioning technologies in agriculture practices such as global navigation satellite systems (GNSS), data collection and management were time-consuming and labor-intensive tasks. Today, due to the introduction of advanced technologies, precise information on the performance of agricultural machines, and smaller autonomous vehicles such as robot mowers, can be collected in a relatively short time. The aim of this work was to track the performance of a robot mower in various turfgrass areas of an equal number of square meters but with four different shapes by using real-time kinematic (RTK)-GNSS devices, and to easily extract data by a custom built software capable of calculating the distance travelled by the robot mower, the forward speed, the cutting area, and the number of intersections of the trajectories. These data were then analyzed in order to provide useful functioning information for manufacturers, entrepreneurs, and practitioners. The path planning of the robot mower was random and the turfgrass area for each of the four shapes was 135 m2 without obstacles. The distance travelled by the robot mower, the mean forward speed, and the intersections of the trajectories were affected by the interaction between the time of cutting and the shape of the turfgrass. For all the different shapes, the whole turfgrass area was completely cut after two hours of mowing. The cutting efficiency decreased by increasing the time, as a consequence of the increase in overlaps. After 75 minutes of cutting, the efficiency was about 35% in all the turfgrass areas shapes, thus indicating a high level of overlapping. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle
Effect of Tillage Systems on Spatial Variation in Soil Chemical Properties and Winter Wheat (Triticum aestivum L.) Performance in Small Fields
Agronomy 2019, 9(4), 182; https://doi.org/10.3390/agronomy9040182 - 10 Apr 2019
Cited by 1
Abstract
To investigate how tillage intensity modifies the small-scale spatial variability of soil and winter wheat parameters, field trials were conducted on small plots (12 m × 35 m) in three temperate environments in the Swiss midlands: Zollikofen in 1999 (loamy silt soil; Gleyic [...] Read more.
To investigate how tillage intensity modifies the small-scale spatial variability of soil and winter wheat parameters, field trials were conducted on small plots (12 m × 35 m) in three temperate environments in the Swiss midlands: Zollikofen in 1999 (loamy silt soil; Gleyic Cambisol) and Schafisheim in 1999 and in 2000 (sandy loam soil; Orthic Luvisol). Total soil nitrogen (Ntot), total carbon (Ctot) and pH were assessed after harvest. A regular nested grid pattern was applied with sampling intervals of 3 m and 1 m at 0–30 cm on a total of nine no-tillage (NT) and nine conventional tillage (CT) plots. At each grid point, wheat biomass, grain yield, N uptake and grain protein concentration were recorded. Small-scale structural variance of soil Ntot, Ctot and pH was slightly larger in NT than in CT in the topsoil in the tillage direction of the field. Wheat traits had a slightly greater small-scale variability in NT than in CT. Spatial relationships between soil and crop parameters were rather weak but more pronounced in NT. Our results suggest limited potential for variable-rate application of N fertilizer and lime for NT soils. Moderate nugget variances in soil parameters were usually higher in CT than in NT, suggesting that differences in spatial patterns between the tillage systems might occur at even smaller scales. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle
A Case-Based Economic Assessment of Robotics Employment in Precision Arable Farming
Agronomy 2019, 9(4), 175; https://doi.org/10.3390/agronomy9040175 - 05 Apr 2019
Cited by 6
Abstract
The need to intensify agriculture to meet increasing nutritional needs, in combination with the evolution of unmanned autonomous systems has led to the development of a series of “smart” farming technologies that are expected to replace or complement conventional machinery and human labor. [...] Read more.
The need to intensify agriculture to meet increasing nutritional needs, in combination with the evolution of unmanned autonomous systems has led to the development of a series of “smart” farming technologies that are expected to replace or complement conventional machinery and human labor. This paper proposes a preliminary methodology for the economic analysis of the employment of robotic systems in arable farming. This methodology is based on the basic processes for estimating the use cost for agricultural machinery. However, for the case of robotic systems, no average norms for the majority of the operational parameters are available. Here, we propose a novel estimation process for these parameters in the case of robotic systems. As a case study, the operation of light cultivation has been selected due the technological readiness for this type of operation. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Review

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Open AccessReview
Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
Agronomy 2020, 10(5), 641; https://doi.org/10.3390/agronomy10050641 - 01 May 2020
Abstract
The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in [...] Read more.
The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed. Full article
(This article belongs to the Special Issue Precision Agriculture)
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