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Application of Satellite and Proximal Sensors in Precision Agriculture

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

Deadline for manuscript submissions: closed (28 February 2018) | Viewed by 53704

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Special Issue Editors

Prof. Dr. Dennis L. Corwin

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Guest Editor

USDA-ARS, U.S. Salinity Laboratory, 450 West Big Springs Road, Riverside, CA 92507, USA
Interests: modeling and mapping of non-point source pollutants using GIS and advanced information technologies; geospatial measurement of apparent soil electrical conductivity (ECa) using electromagnetic induction and electrical resistivity measurements to direct soil sampling for characterization of spatial variability; ECa-directed soil sampling for precision agriculture applications, soil quality assessment, monitoring management—induced changes, and monitoring climate change impacts on soil; degraded water reuse sustainability and impact on soil properties; field to regional scale salinity assessment using satellite imagery and proximal sensors

Dr. Elia Scudiero

E-Mail Website
Guest Editor

Department of Environmental Sciences, University of California-Riverside, CA and USDA-ARS, U.S. Salinity Laboratory, 450 West Big Springs Road, Riverside, CA 92507, USA
Interests: use of geophysical (near-ground and remote) measurements to characterize and model multi-scale (from field to national) agro-environmental soil–plant processes to support sustainable agriculture and water management practices

Special Issue Information

Dear Colleagues,

Sustainable agriculture is considered the most viable means of meeting future food needs for the world’s increasing population through its goal of delicately balancing crop productivity, profitability, natural resource utilization, sustainability of the soil–plant–water environment, and environmental impacts. Precision agriculture is a proposed approach for achieving sustainable agriculture. Precision agriculture utilizes rapidly-evolving information and electronic technologies to modify the management of soils, pests, and crops in a site-specific manner as conditions within a field change spatially and temporarily. Satellite imagery and proximal sensors provide rapid temporal and spatial measurements to characterize within-field variability of pests, crops, and edaphic properties for application to precision agriculture. The collection of papers that comprises this Special Issue of Sensors provides a review of the current technology and understanding of satellite imagery and proximal sensors used for application in precision agriculture.

The objective of this Special Issue is to present state-of-the-art research on precision agriculture applications of satellite and proximal sensing. Submissions on the use of satellite and proximal sensors for the following topics (but not limited to these topics) are invited: delineation of site-specific management units (e.g., water, agrochemicals), digital soil mapping, monitoring management-induced soil changes, remote and proximal data assimilation in crop growth models, detection and mapping of matric and osmotic crop stress, detection and mapping of other crop stressors (i.e., pests, disease, nutrient deficiency), crop yield models, assessment of environmental impacts of agriculture, quantitative remote sensing for mapping agricultural parameters (e.g. evapotranspiration, nutrients, trace elements, salinity), spatial sampling, spatial statistics, geostatistics, yield mapping, sensor-based variable rate irrigation and nitrogen application, and crop water use mapping.

Papers must present innovative and original research.

Prof. Dr. Dennis L. Corwin
Dr. Elia Scudiero
Guest Editors

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Keywords

Soil spatial variability
site-specific management
near-ground sensing
airborne sensing
remote sensing
spatial statistics
variable-rate management
soil-plant-atmosphere modeling
precision agriculture
geographic information system

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Published Papers (8 papers)

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Research

16 pages, 3306 KiB

Open AccessArticle

Partial Least Square Discriminant Analysis Based on Normalized Two-Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets

by Yue Shi, Wenjiang Huang, Huichun Ye, Chao Ruan, Naichen Xing, Yun Geng, Yingying Dong and Dailiang Peng

Sensors 2018, 18(6), 1901; https://doi.org/10.3390/s18061901 - 11 Jun 2018

Cited by 48 | Viewed by 6049

Abstract

In recent decades, rice disease co-epidemics have caused tremendous damage to crop production in both China and Southeast Asia. A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate-resolution imagery. However, the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data. The aims of this study were (1) to develop a set of normalized two-stage vegetation indices (VIs) for characterizing the progressive development of different diseases with rice; (2) to explore the performance of combined normalized two-stage VIs in partial least square discriminant analysis (PLS-DA); and (3) to map and evaluate the damage caused by rice diseases at fine spatial scales, for the first time using bi-temporal, high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution. Our findings suggest that the primary biophysical parameters caused by different disease (e.g., changes in leaf area, pigment contents, or canopy morphology) can be captured using combined normalized two-stage VIs. PLS-DA was able to classify rice diseases at a sub-field scale, with an overall accuracy of 75.62% and a Kappa value of 0.47. The approach was successfully applied during a typical co-epidemic outbreak of rice dwarf (Rice dwarf virus, RDV), rice blast (Magnaporthe oryzae), and glume blight (Phyllosticta glumarum) in Guangxi Province, China. Furthermore, our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents. Full article

(This article belongs to the Special Issue Application of Satellite and Proximal Sensors in Precision Agriculture)

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16 pages, 32991 KiB

Open AccessArticle

A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network

by Hadi Karimi, Søren Skovsen, Mads Dyrmann and Rasmus Nyholm Jørgensen

Sensors 2018, 18(5), 1611; https://doi.org/10.3390/s18051611 - 18 May 2018

Cited by 5 | Viewed by 3668

Abstract

Determining the individual location of a plant, besides evaluating sowing performance, would make subsequent treatment for each plant across a field possible. In this study, a system for locating cereal plant stem emerging points (PSEPs) has been developed. In total, 5719 images were gathered from several cereal fields. In 212 of these images, the PSEPs of the cereal plants were marked manually and used to train a fully-convolutional neural network. In the training process, a cost function was made, which incorporates predefined penalty regions and PSEPs. The penalty regions were defined based on fault prediction of the trained model without penalty region assignment. By adding penalty regions to the training, the network’s ability to precisely locate emergence points of the cereal plants was enhanced significantly. A coefficient of determination of about 87 percent between the predicted PSEP number of each image and the manually marked one implies the ability of the system to count PSEPs. With regard to the obtained results, it was concluded that the developed model can give a reliable clue about the quality of PSEPs’ distribution and the performance of seed drills in fields. Full article

(This article belongs to the Special Issue Application of Satellite and Proximal Sensors in Precision Agriculture)

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13 pages, 10322 KiB

Open AccessArticle

Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

by Nima Teimouri, Mads Dyrmann, Per Rydahl Nielsen, Solvejg Kopp Mathiassen, Gayle J. Somerville and Rasmus Nyholm Jørgensen

Sensors 2018, 18(5), 1580; https://doi.org/10.3390/s18051580 - 16 May 2018

Cited by 75 | Viewed by 8920

Abstract

This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516 images, which also varied in term of crop, soil type, image resolution and light conditions. The overall performance of this approach achieved a maximum accuracy of 78% for identifying Polygonum spp. and a minimum accuracy of 46% for blackgrass. In addition, it achieved an average 70% accuracy rate in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species. Full article

(This article belongs to the Special Issue Application of Satellite and Proximal Sensors in Precision Agriculture)

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25 pages, 38610 KiB

Open AccessArticle

A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms

by Mohamed Hassanein, Zahra Lari and Naser El-Sheimy

Sensors 2018, 18(4), 1253; https://doi.org/10.3390/s18041253 - 18 Apr 2018

Cited by 58 | Viewed by 6049

Abstract

Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significantly in different applications as it provides a special platform capable of combining the benefits of terrestrial and aerial remote sensing. Therefore, such technology has been established as an important source of data collection for different precision agriculture (PA) applications such as crop health monitoring and weed management. Generally, these PA applications depend on performing a vegetation segmentation process as an initial step, which aims to detect the vegetation objects in collected agriculture fields’ images. The main result of the vegetation segmentation process is a binary image, where vegetations are presented in white color and the remaining objects are presented in black. Such process could easily be performed using different vegetation indexes derived from multispectral imagery. Recently, to expand the use of UAV imagery systems for PA applications, it was important to reduce the cost of such systems through using low-cost RGB cameras Thus, developing vegetation segmentation techniques for RGB images is a challenging problem. The proposed paper introduces a new vegetation segmentation methodology for low-cost UAV RGB images, which depends on using Hue color channel. The proposed methodology follows the assumption that the colors in any agriculture field image can be distributed into vegetation and non-vegetations colors. Therefore, four main steps are developed to detect five different threshold values using the hue histogram of the RGB image, these thresholds are capable to discriminate the dominant color, either vegetation or non-vegetation, within the agriculture field image. The achieved results for implementing the proposed methodology showed its ability to generate accurate and stable vegetation segmentation performance with mean accuracy equal to 87.29% and standard deviation as 12.5%. Full article

(This article belongs to the Special Issue Application of Satellite and Proximal Sensors in Precision Agriculture)

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13 pages, 15189 KiB

Open AccessArticle

Assessing the Crop-Water Status in Almond (Prunus dulcis Mill.) Trees via Thermal Imaging Camera Connected to Smartphone

by Iván Francisco García-Tejero, Carlos José Ortega-Arévalo, Manuel Iglesias-Contreras, José Manuel Moreno, Luciene Souza, Simón Cuadros Tavira and Víctor Hugo Durán-Zuazo

Sensors 2018, 18(4), 1050; https://doi.org/10.3390/s18041050 - 31 Mar 2018

Cited by 43 | Viewed by 5888

Abstract

Different tools are being implemented in order to improve the water management in agricultural irrigated areas of semiarid environments. Thermography has been progressively introduced as a promising technique for irrigation scheduling and the assessing of crop-water status, especially when deficit irrigation is being implemented. However, an important limitation is related to the cost of the actual cameras, this being a severe limitation to its practical usage by farmers and technicians. This work evaluates the potential and the robustness of a thermal imaging camera that is connected to smartphone (Flir One) recently developed by Flir Systems Inc. as a first step to assess the crop water status. The trial was developed in mature almond (Prunus dulcis Mill.) trees that are subjected to different irrigation treatments. Thermal information obtained by the Flir One camera was deal with the thermal information obtained with a conventional Thermal Camera (Flir SC660) with a high resolution, and subsequently, confronted with other related plant physiological parameters (leaf water potential, Ψ_leaf, and stomatal conductance, g_s). Thermal imaging camera connected to smartphone provided useful information in estimating the crop-water status in almond trees, being a potential promising tool to accelerate the monitoring process and thereby enhance water-stress management of almond orchards. Full article

(This article belongs to the Special Issue Application of Satellite and Proximal Sensors in Precision Agriculture)

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19 pages, 7912 KiB

Open AccessArticle

New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery

by Qiong Zheng, Wenjiang Huang, Ximin Cui, Yue Shi and Linyi Liu

Sensors 2018, 18(3), 868; https://doi.org/10.3390/s18030868 - 15 Mar 2018

Cited by 135 | Viewed by 10944

Abstract

Yellow rust is one of the most destructive diseases for winter wheat and has led to a significant decrease in winter wheat quality and yield. Identifying and monitoring yellow rust is of great importance for guiding agricultural production over large areas. Compared with traditional crop disease discrimination methods, remote sensing technology has proven to be a useful tool for accomplishing such a task at large scale. This study explores the potential of the Sentinel-2 Multispectral Instrument (MSI), a newly launched satellite with refined spatial resolution and three red-edge bands, for discriminating between yellow rust infection severities (i.e., healthy, slight, and severe) in winter wheat. The corresponding simulative multispectral bands for the Sentinel-2 sensor were calculated by the sensor’s relative spectral response (RSR) function based on the in situ hyperspectral data acquired at the canopy level. Three Sentinel-2 spectral bands, including B4 (Red), B5 (Re1), and B7 (Re3), were found to be sensitive bands using the random forest (RF) method. A new multispectral index, the Red Edge Disease Stress Index (REDSI), which consists of these sensitive bands, was proposed to detect yellow rust infection at different severity levels. The overall identification accuracy for REDSI was 84.1% and the kappa coefficient was 0.76. Moreover, REDSI performed better than other commonly used disease spectral indexes for yellow rust discrimination at the canopy scale. The optimal threshold method was adopted for mapping yellow rust infection at regional scales based on realistic Sentinel-2 multispectral image data to further assess REDSI’s ability for yellow rust detection. The overall accuracy was 85.2% and kappa coefficient was 0.67, which was found through validation against a set of field survey data. This study suggests that the Sentinel-2 MSI has the potential for yellow rust discrimination, and the newly proposed REDSI has great robustness and generalized ability for yellow rust detection at canopy and regional scales. Furthermore, our results suggest that the above remote sensing technology can be used to provide scientific guidance for monitoring and precise management of crop diseases and pests. Full article

(This article belongs to the Special Issue Application of Satellite and Proximal Sensors in Precision Agriculture)

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24 pages, 37076 KiB

Open AccessArticle

A Holistic Approach to the Evaluation of the Montado Ecosystem Using Proximal Sensors

by João Serrano, Shakib Shahidian, José Marques da Silva and Mário De Carvalho

Sensors 2018, 18(2), 570; https://doi.org/10.3390/s18020570 - 13 Feb 2018

Cited by 7 | Viewed by 4983

Abstract

The Montado is a silvo-pastoral system characterized by open canopy woodlands with natural or cultivated grassland in the undercover and grazing animals. The aims of this study were to present several proximal sensors with potential to monitor relevant variables in the complex montado ecosystem and demonstrate their application in a case study designed to evaluate the effect of trees on the pasture. This work uses data collected between March and June 2016, at peak of dryland pasture production under typical Mediterranean conditions, in twenty four sampling points, half under tree canopy (UTC) and half outside tree canopy (OTC). Correlations were established between pasture biomass and capacitance measured by a commercial probe and between pasture quality and normalized difference vegetation index (NDVI) measured by a commercial active optical sensor. The interest of altimetric and apparent soil electrical conductivity maps as the first step in the implementation of precision agriculture projects was demonstrated. The use of proximal sensors to monitor soil moisture content, pasture photosynthetically active radiation and temperature helped to explain the influence of trees on pasture productivity and quality. The significant and strong correlations obtained between capacitance and pasture biomass and between NDVI and pasture nutritive value (in terms of crude protein, CP and neutral detergent fibre, NDF) can make an important contribution to determination of key components of pasture productivity and quality and implementation of site-specific pasture management. Animal tracking demonstrated its potential to be an important tool for understanding the interaction between various factors and components that interrelate in the montado ecosystem and to support grazing management decisions. Full article

(This article belongs to the Special Issue Application of Satellite and Proximal Sensors in Precision Agriculture)

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15 pages, 11731 KiB

Open AccessArticle

Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China

by Haifeng Tian, Mingquan Wu, Li Wang and Zheng Niu

Sensors 2018, 18(1), 185; https://doi.org/10.3390/s18010185 - 11 Jan 2018

Cited by 72 | Viewed by 6162

Abstract

Areas and spatial distribution information of paddy rice are important for managing food security, water use, and climate change. However, there are many difficulties in mapping paddy rice, especially mapping multi-season paddy rice in rainy regions, including differences in phenology, the influence of weather, and farmland fragmentation. To resolve these problems, a novel multi-season paddy rice mapping approach based on Sentinel-1A and Landsat-8 data is proposed. First, Sentinel-1A data were enhanced based on the fact that the backscattering coefficient of paddy rice varies according to its growth stage. Second, cropland information was enhanced based on the fact that the NDVI of cropland in winter is lower than that in the growing season. Then, paddy rice and cropland areas were extracted using a K-Means unsupervised classifier with enhanced images. Third, to further improve the paddy rice classification accuracy, cropland information was utilized to optimize distribution of paddy rice by the fact that paddy rice must be planted in cropland. Classification accuracy was validated based on ground-data from 25 field survey quadrats measuring 600 m × 600 m. The results show that: multi-season paddy rice planting areas effectively was extracted by the method and adjusted early rice area of 1630.84 km², adjusted middle rice area of 556.21 km², and adjusted late rice area of 3138.37 km². The overall accuracy was 98.10%, with a kappa coefficient of 0.94. Full article

(This article belongs to the Special Issue Application of Satellite and Proximal Sensors in Precision Agriculture)

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