Special Issue "Remote Sensing for Sustainable Agriculture and Smart Farming"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 July 2020).

Special Issue Editors

Prof. Dr. Andy Nelson
Website
Guest Editor
Department of Natural Resources, ITC - Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands
Interests: crop management practices; spatial accessibility models; pest and disease modelling; crop stress characterisation; crop yield estimation
Dr. Mirco Boschetti
Website
Guest Editor
Institute for Electromagnetic Sensing of the Environment, Italian National Research Council, (IREA-CNR), 7 - 00185 Roma, Italy
Interests: land use and crop mapping; retrieval of biophysical parameters; agro-practices and phenological monitoring from time series analysis; environmental indicator through geographic multisource data integration; precision farming; anomaly detection
Special Issues and Collections in MDPI journals
Assoc. Prof. Adam Sparks
Website
Guest Editor
University of Southern Queensland, Centre for Crop Health, Toowoomba, QLD 4350, Australia
Interests: plant pathology; agricultural spatial analysis and modelling, crop and pasture protection (pests, diseases and weeds)

Special Issue Information

Dear Colleagues,

The agricultural sector is one of the largest contributors to greenhouse gas emissions and is a major consumer of resources and energy. It is also a driver of land-use change, natural resource degradation and habitat loss. Continuing to produce sufficient and nutritious food whilst reducing environmental impacts will require changes in crop and livestock management. These changes must increase efficiency and reduce losses to ensure a stable and sustainable production in the context of global change (climate, technological, economic and demographic).

Smart farming, or smart agriculture, is the use of information technology to increase the quantity and quality of agricultural products in an optimal way that preserves natural resource capital and ecosystem functionality. Information from a range of sensor technologies together with innovative approaches to extract added-value information can be used for strategic decision-making at farm and field levels and for tactical guiding of in-season operational activities down to the individual plant level.

With respect to smart farming, remote sensing can provide: (i) information on pre-season risk factors for crop/livestock health and productivity, (ii) within-season observations of the current conditions of crops, livestock, soil and water, and (iii) information on the effect of treatments, interventions or other events–such as lodging—that take place during the season. All of these can help guide preventative or corrective actions for the current season as well as management decisions for the following seasons.

This Special Issue is inviting manuscripts that demonstrate the potential of remote sensing sources for smart-farming applications. We particularly invite submissions that combine information from remote sensing with information from on-farm sensors. Topics include:

  • Spatial modelling for pre-season assessments of crop and livestock health risk factors
  • Analytics for crop, water, soil conditions
  • Early detection, diagnostics, control of crop pests and diseases
  • Early detection, diagnostics, control of nutrient deficiencies
  • In-season assessment of bio-physical crop parameters as quantitative information for precision-farming decision
  • Detection of shifts or movements in pest and disease distributions
  • Forecasting of farm/field-level yield and mapping of within-field yield variability
  • Detection of the causes of yield variability within fields and farms
  • Impact assessments of new agricultural technologies

Prof. Andy Nelson
Dr. Mirco Boschetti
Assoc. Prof. Adam H. Sparks
Guest Editors

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. Remote Sensing 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 2400 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

  • Smart agriculture/farming
  • Precision agriculture/farming
  • Crop biophysical parameter estimation
  • Multi sensor approaches
  • Spatial quantification
  • Crop health
  • Livestock health
  • Agricultural technology
  • Early detection
  • Crop health diagnostics
  • Pests and diseases
  • Yield variability
  • Impact assessments

Published Papers (8 papers)

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Open AccessArticle
Application of Color Featuring and Deep Learning in Maize Plant Detection
Remote Sens. 2020, 12(14), 2229; https://doi.org/10.3390/rs12142229 - 11 Jul 2020
Cited by 1
Abstract
Maize plant detection was conducted in this study with the goals of target fertilization and reduction of fertilization waste in weed spots and gaps between maize plants. The methods used included two types of color featuring and deep learning (DL). The four color [...] Read more.
Maize plant detection was conducted in this study with the goals of target fertilization and reduction of fertilization waste in weed spots and gaps between maize plants. The methods used included two types of color featuring and deep learning (DL). The four color indices used were excess green (ExG), excess red (ExR), ExG minus ExR, and the hue value from the HSV (hue, saturation, and value) color space, while the DL methods used were YOLOv3 and YOLOv3_tiny. For practical application, this study focused on performance comparison in detection accuracy, robustness to complex field conditions, and detection speed. Detection accuracy was evaluated by the resulting images, which were divided into three categories: true positive, false positive, and false negative. The robustness evaluation was performed by comparing the average intersection over union of each detection method across different sub–datasets—namely original subset, blur processing subset, increased brightness subset, and reduced brightness subset. The detection speed was evaluated by the indicator of frames per second. Results demonstrated that the DL methods outperformed the color index–based methods in detection accuracy and robustness to complex conditions, while they were inferior to color feature–based methods in detection speed. This research shows the application potential of deep learning technology in maize plant detection. Future efforts are needed to improve the detection speed for practical applications. Full article
(This article belongs to the Special Issue Remote Sensing for Sustainable Agriculture and Smart Farming)
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Open AccessFeature PaperArticle
Influence of Soil Properties on Maize and Wheat Nitrogen Status Assessment from Sentinel-2 Data
Remote Sens. 2020, 12(14), 2175; https://doi.org/10.3390/rs12142175 - 08 Jul 2020
Abstract
Soil properties variability is a factor that greatly influences cereals crops production and interacts with a proper assessment of crop nutritional status, which is fundamental to support site-specific management able to guarantee a sustainable crop production. Several management strategies of precision agriculture are [...] Read more.
Soil properties variability is a factor that greatly influences cereals crops production and interacts with a proper assessment of crop nutritional status, which is fundamental to support site-specific management able to guarantee a sustainable crop production. Several management strategies of precision agriculture are now available to adjust the nitrogen (N) input to the actual crop needs. Many of the methods have been developed for proximal sensors, but increasing attention is being given to satellite-based N management systems, many of which rely on the assessment of the N status of crops. In this study, the reliability of the crop nutritional status assessment through the estimation of the nitrogen nutrition index (NNI) from Sentinel-2 (S2) satellite images was examined, focusing of the impact of soil properties variability for crop nitrogen deficiency monitoring. Vegetation indices (VIs) and biophysical variables (BVs), such as the green area index (GAI_S2), leaf chlorophyll content (Cab_S2), and canopy chlorophyll content (CCC_S2), derived from S2 imagery, were used to investigate plant N status and NNI retrieval, in the perspective of its use for guiding site-specific N fertilization. Field experiments were conducted on maize and on durum wheat, manipulating 4 groups of plots, according to soil characteristics identified by a soil map and quantified by soil samples analysis, with different N treatments. Field data collection highlighted different responses of the crops to N rate and soil type in terms of NNI, biomass (W), and nitrogen concentration (Na%). For both crops, plots in one soil class (FOR1) evidenced considerably lower values of BVs and stress conditions with respect to others soil classes even for high N rates. Soil samples analyses showed for FOR1 soil class statistically significant differences for pH, compared to the other soil classes, indicating that this property could be a limiting factor for nutrient absorption, hence crop growth, regardless of the amount of N distributed to the crop. The correlation analysis between measured crop related BVs and satellite-based products (VIs and S2_BVs) shows that it is possible to: (i) directly derive NNI from CCC_S2 (R2 = 0.76) and either normalized difference red edge index (NDRE) for maize (R2 = 0.79) or transformed chlorophyll absorption ratio index (TCARI) for durum wheat (R2 = 0.61); (ii) indirectly estimate NNI as the ratio of plant nitrogen uptake (PNUa) and critical plant nitrogen uptake (PNUc) derived using CCC_S2 (R2 = 0.77) and GAI_S2 (R2 = 0.68), respectively. Results of this study confirm that NNI is a good indicator to monitor plants N status, but also highlights the importance of linking this information to soil properties to support N site-specific fertilization in the precision agriculture framework. These findings contribute to rational agro-practices devoted to avoid N fertilization excesses and consequent environmental losses, bringing out the real limiting factors for optimal crop growth. Full article
(This article belongs to the Special Issue Remote Sensing for Sustainable Agriculture and Smart Farming)
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Open AccessArticle
Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn
Remote Sens. 2020, 12(10), 1597; https://doi.org/10.3390/rs12101597 - 17 May 2020
Cited by 1
Abstract
Determining the optimal nitrogen (N) rate in corn remains a critical issue, mainly due to unaccounted spatial (e.g., soil properties) and temporal (e.g., weather) variability. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors may provide opportunities to improve N management by the timely [...] Read more.
Determining the optimal nitrogen (N) rate in corn remains a critical issue, mainly due to unaccounted spatial (e.g., soil properties) and temporal (e.g., weather) variability. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors may provide opportunities to improve N management by the timely informing of spatially variable, in-season N applications. Here, we developed a practical decision support system (DSS) to translate spatial field characteristics and normalized difference red edge (NDRE) values into an in-season N application recommendation. On-farm strip-trials were established at three sites over two years to compare farmer’s traditional N management to a split-application N management guided by our UAV sensor-based DSS. The proposed systems increased nitrogen use efficiency 18.3 ± 6.1 kg grain kg N−1 by reducing N rates by 31 ± 6.3 kg N ha−1 with no yield differences compared to the farmers’ traditional management. We identify five avenues for further improvement of the proposed DSS: definition of the initial base N rate, estimation of inputs for sensor algorithms, management zone delineation, high-resolution image normalization approach, and the threshold for triggering N application. Two virtual reference (VR) methods were compared with the high N (HN) reference strip method for normalizing high-resolution sensor data. The VR methods resulted in significantly lower sufficiency index values than those generated by the HN reference, resulting in N fertilization recommendations that were 31.4 ± 10.3 kg ha−1 higher than the HN reference N fertilization recommendation. The use of small HN reference blocks in contrasting management zones may be more appropriate to translate field-scale, high-resolution imagery into in-season N recommendations. In view of a growing interest in using UAVs in commercial fields and the need to improve crop NUE, further work is needed to refine approaches for translating imagery into in-season N recommendations. Full article
(This article belongs to the Special Issue Remote Sensing for Sustainable Agriculture and Smart Farming)
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Open AccessArticle
Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue
Remote Sens. 2019, 11(21), 2583; https://doi.org/10.3390/rs11212583 - 04 Nov 2019
Cited by 3
Abstract
Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil [...] Read more.
Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil moisture content. In some areas of the world, the management of soil surface residue (SSR) is crucial for increasing soil fertility, maintaining high soil carbon levels, and reducing the degradation of soil due to rain and wind erosion. Standard methods of measuring the residue cover are time and labor intensive, but remote sensing can support the monitoring of conservation tillage practices applied to large fields. We investigated the potential of per-pixel and object-based image analysis (OBIA) for detecting and estimating the coverage of SSRs after tillage and planting practices for agricultural research fields in Iran using tillage indices for Landsat-8 and novel indices for Sentinel-2A. For validation, SSR was measured in the field through line transects at the beginning of the agricultural season (prior to autumn crop planting). Per-pixel approaches for Landsat-8 satellite images using normalized difference tillage index (NDTI) and simple tillage index (STI) yielded coefficient of determination (R2) values of 0.727 and 0.722, respectively. We developed comparable novel indices for Sentinel-2A satellite data that yielded R2 values of 0.760 and 0.759 for NDTI and STI, respectively, which means that the Sentinel data better matched the ground truth data. We tested several OBIA methods and achieved very high overall accuracies of up to 0.948 for Sentinel-2A and 0.891 for Landsat-8 with a membership function method. The OBIA methods clearly outperformed per-pixel approaches in estimating SSR and bear the potential to substitute or complement ground-based techniques. Full article
(This article belongs to the Special Issue Remote Sensing for Sustainable Agriculture and Smart Farming)
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Open AccessArticle
Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions
Remote Sens. 2019, 11(21), 2546; https://doi.org/10.3390/rs11212546 - 30 Oct 2019
Cited by 7
Abstract
Color segmentation is one of the most thoroughly studied problems in agricultural applications of remote image capture systems, since it is the key step in several different tasks, such as crop harvesting, site specific spraying, and targeted disease control under natural light. This [...] Read more.
Color segmentation is one of the most thoroughly studied problems in agricultural applications of remote image capture systems, since it is the key step in several different tasks, such as crop harvesting, site specific spraying, and targeted disease control under natural light. This paper studies and compares five methods to segment plum fruit images under ambient conditions at 12 different light intensities, and an ensemble method combining them. In these methods, several color features in different color spaces are first extracted for each pixel, and then the most effective features are selected using a hybrid approach of artificial neural networks and the cultural algorithm (ANN-CA). The features selected among the 38 defined channels were the b* channel of L*a*b*, and the color purity index, C*, from L*C*h. Next, fruit/background segmentation is performed using five classifiers: artificial neural network-imperialist competitive algorithm (ANN-ICA); hybrid artificial neural network-harmony search (ANN-HS); support vector machines (SVM); k nearest neighbors (kNN); and linear discriminant analysis (LDA). In the ensemble method, the final class for each pixel is determined using the majority voting method. The experiments showed that the correct classification rate for the majority voting method excluding LDA was 98.59%, outperforming the results of the constituent methods. Full article
(This article belongs to the Special Issue Remote Sensing for Sustainable Agriculture and Smart Farming)
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Open AccessArticle
Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation
Remote Sens. 2019, 11(15), 1763; https://doi.org/10.3390/rs11151763 - 26 Jul 2019
Cited by 17
Abstract
Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with [...] Read more.
Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with great flexibility and applicability. This study aimed to determine the feasibility of combining color and texture information derived from UAV-based digital images for estimating LAI of rice (Oryza sativa L.). Rice field trials were conducted at two sites using different nitrogen application rates, varieties, and transplanting methods during 2016 to 2017. Digital images were collected using a consumer-grade UAV after sampling at key growth stages of tillering, stem elongation, panicle initiation and booting. Vegetation color indices (CIs) and grey level co-occurrence matrix-based textures were extracted from mosaicked UAV ortho-images for each plot. As a solution of using indices composed by two different textures, normalized difference texture indices (NDTIs) were calculated by two randomly selected textures. The relationships between rice LAIs and each calculated index were then compared using simple linear regression. Multivariate regression models with different input sets were further used to test the potential of combining CIs with various textures for rice LAI estimation. The results revealed that the visible atmospherically resistant index (VARI) based on three visible bands and the NDTI based on the mean textures derived from the red and green bands were the best for LAI retrieval in the CI and NDTI groups, respectively. Independent accuracy assessment showed that random forest (RF) exhibited the best predictive performance when combining CI and texture inputs (R2 = 0.84, RMSE = 0.87, MAE = 0.69). This study introduces a promising solution of combining color indices and textures from UAV-based digital imagery for rice LAI estimation. Future studies are needed on finding the best operation mode, suitable ground resolution, and optimal predictive methods for practical applications. Full article
(This article belongs to the Special Issue Remote Sensing for Sustainable Agriculture and Smart Farming)
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Open AccessArticle
Finite Difference Analysis and Bivariate Correlation of Hyperspectral Data for Detecting Laurel Wilt Disease and Nutritional Deficiency in Avocado
Remote Sens. 2019, 11(15), 1748; https://doi.org/10.3390/rs11151748 - 25 Jul 2019
Cited by 8
Abstract
Laurel wilt (Lw) is a very destructive disease and poses a serious threat to the commercial production of avocado in Florida, USA. External symptoms of Lw are similar to those that are caused by other diseases and disorders. A rapid technique to distinguish [...] Read more.
Laurel wilt (Lw) is a very destructive disease and poses a serious threat to the commercial production of avocado in Florida, USA. External symptoms of Lw are similar to those that are caused by other diseases and disorders. A rapid technique to distinguish Lw infected avocado from healthy trees and trees with other abiotic stressors is presented in this paper. A novel method was developed to analyze data from hyperspectral data using finite difference approximation (FDA) and bivariate correlation (BC) to discriminate Lw, Nitrogen (N), and Iron (Fe) deficiencies from healthy avocado plants. Several combinatorial methods were used in preprocessing the data, such as standard normal transformation of data, smoothing of the data, and polynomial fit. The FDA technique was derived using a Taylor Polynomial finite difference approximation. This FDA accentuates inflection points in the spectrum. These, in turn, reveal variance in the data that can be used to identify spectral signature associated with healthy and diseased states. By statistical correlation using the bivariate correlation coefficient of these enhanced spectral patterns, an algorithm (FDA-BC) for distinguishing Lw avocado leaves from all other categories of healthy or mineral deficient avocado leaves is achieved with an overall accuracy of 100%. Full article
(This article belongs to the Special Issue Remote Sensing for Sustainable Agriculture and Smart Farming)
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Open AccessTechnical Note
Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-Principle
Remote Sens. 2019, 11(21), 2541; https://doi.org/10.3390/rs11212541 - 29 Oct 2019
Cited by 4
Abstract
In this paper we aim to show a proof-of-principle approach to detect and monitor weed management using glyphosate-based herbicides in agricultural practices. In a case study in Germany, we demonstrate the application of Sentinel-2 multispectral time-series data. Spectral broadband vegetation indices were analysed [...] Read more.
In this paper we aim to show a proof-of-principle approach to detect and monitor weed management using glyphosate-based herbicides in agricultural practices. In a case study in Germany, we demonstrate the application of Sentinel-2 multispectral time-series data. Spectral broadband vegetation indices were analysed to observe vegetation traits and weed damage arising from herbicide-based management. The approach has been validated with stakeholder information about herbicide treatment using commercial products. As a result, broadband NDVI calculated from Sentinel-2 data showed explicit feedback after the glyphosate-based herbicide treatment. Vegetation damage could be detected after just two days following of glyphosate-based herbicide treatment. This trend was observed in three different application scenarios, i.e., during growing stage, before harvest and after harvest. The findings of the study demonstrate the feasibility of satellite based broadband NDVI data for the detection of glyphosate-based herbicide treatment and, e.g., the monitoring of latency to harvesting. The presented results can be used to implement monitoring concepts to provide the necessary transparency about weed treatment in agricultural practices and to support environmental monitoring. Full article
(This article belongs to the Special Issue Remote Sensing for Sustainable Agriculture and Smart Farming)
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