Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Proximal Sensing for Nitrogen Management

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 22814

Share This Special Issue

Special Issue Editors

Prof. Dr. Francisco M. Padilla

E-Mail Website
Guest Editor

Department of Agronomy, University of Almeria, Carretera de Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
Interests: optical sensors; plant-soil interactions; precision agriculture; N fertilization

Prof. Dr. Rodney B. Thompson

E-Mail Website
Guest Editor

Department of Agronomy, University of Almeria, Carretera de Sacramento s/n, 04120 La Cañada de San Urbano, Almería, Spain
Interests: optimising crop N management; monitoring crop N status; fertigation

Special Issue Information

Dear Colleagues,

This Special Issue on “Proximal Sensing for Nitrogen Management” welcomes the submission of both review and original research articles that focus on the evaluation and use of proximal optical sensors for (a) assessment of crop N status and (b) assisting in the determination of crop N fertiliser requirements. This Special Issue is open to research conducted in all types of crops, such as cereals, vegetables, fruit, fibre, or ornamental crops, and that employ optical sensors such as chlorophyll meters, passive or active canopy reflectance sensors, or chlorophyll fluorescence sensors. Articles on other types of proximal optical sensors are welcome. In this Special Issue, proximal optical sensors are regarded as a form of remote sensing in which the sensors are positioned either in contact with or close to the crop, i.e., within several meters. Studies using both or comparing proximal approaches with remote-sensing approches (aircraft or satellite mounted) are welcome. In order to maintain a focus on proximal sensors, studies based solely on remote sensing or sensors mounted on unmanned aerial vehicles or drone platforms are discouraged. Contributions integrating long-time records and multi-year data are especially welcome, as are contributions dealing with the practical application of proximal optical sensors for crop N management.

Prof. Francisco M. Padilla
Prof. Rodney B. Thompson
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 submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

Canopy reflectance
Chlorophyll fluorescence
Chlorophyll meters
Crop monitoring
Fertilization
Nitrogen management
Optical sensors
Plant nutrition
Precision agriculture
Proximal sensing

Published Papers (6 papers)

Download All Papers

Research

Jump to: Review

20 pages, 4211 KiB

Open AccessArticle

Effect of Cultivar on Chlorophyll Meter and Canopy Reflectance Measurements in Cucumber

by Romina de Souza, Rafael Grasso, M. Teresa Peña-Fleitas, Marisa Gallardo, Rodney B. Thompson and Francisco M. Padilla

Sensors 2020, 20(2), 509; https://doi.org/10.3390/s20020509 - 16 Jan 2020

Cited by 11 | Viewed by 3040

Abstract

Optical sensors can be used to assess crop N status to assist with N fertilizer management. Differences between cultivars may affect optical sensor measurement. Cultivar effects on measurements made with the SPAD-502 (Soil Plant Analysis Development) meter and the MC-100 (Chlorophyll Concentration Meter), and of several vegetation indices measured with the Crop Circle ACS470 canopy reflectance sensor, were assessed. A cucumber (Cucumis sativus L.) crop was grown in a greenhouse, with three cultivars. Each cultivar received three N treatments, of increasing N concentration, being deficient (N1), sufficient (N2) and excessive (N3). There were significant differences between cultivars in the measurements made with both chlorophyll meters, particularly when N supply was sufficient and excessive (N2 and N3 treatments, respectively). There were no consistent differences between cultivars in vegetation indices. Optical sensor measurements were strongly linearly related to leaf N content in each of the three cultivars. The lack of a consistent effect of cultivar on the relationship with leaf N content suggests that a unique equation to estimate leaf N content from vegetation indices can be applied to all three cultivars. Results of chlorophyll meter measurements suggest that care should be taken when using sufficiency values, determined for a particular cultivar Full article

(This article belongs to the Special Issue Proximal Sensing for Nitrogen Management)

► Show Figures

Figure 1

25 pages, 2950 KiB

Open AccessArticle

Sources of Variation in Assessing Canopy Reflectance of Processing Tomato by Means of Multispectral Radiometry

by Giorgio Gianquinto, Francesco Orsini, Giuseppina Pennisi and Stefano Bona

Sensors 2019, 19(21), 4730; https://doi.org/10.3390/s19214730 - 31 Oct 2019

Cited by 10 | Viewed by 3034

Abstract

Canopy reflectance sensors are a viable technology to optimize the fertilization management of crops. In this research, canopy reflectance was measured through a passive sensor to evaluate the effects of either crop features (N fertilization, soil mulching, appearance of red fruits, and cultivars) or sampling methods (sampling size, sensor position, and hour of sampling) on the reliability of vegetation indices (VIs). Sixteen VIs were derived, including seven simple wavelength reflectance ratios (NIR/R460, NIR/R510, NIR/R560, NIR/R610, NIR/R660, NIR/R710, NIR/R760), seven normalized indices (NDVI, G-NDVI, MCARISAVI, OSAVI, TSAVI, TCARI), and two combined indices (TCARI/OSAVI; MCARI/OSAVI). NIR/560 and G-NDVI (Normalized Difference Vegetation Index on Greenness) were the most reliable in discriminating among fertilization rates, with results unaffected by the appearance of maturing fruits, and the most stable in response to different cultivars. Black mulching film did not affect NIR/560 and G-NDVI behavior at the beginning of the growing season, when the crop is more responsive to N management. Due to a moderate variability of NIR/560 and G-NDVI, a small sample size (5–10 observations) is sufficient to obtain reliable measurements. Performing the measurements at 11:00 and 14:00 and maintaining a greater distance (1.8 m) between plants and instrument enhanced measurement consistency. Accordingly, NIR/560 and G-NDVI resulted in the most reliable VIs. Full article

(This article belongs to the Special Issue Proximal Sensing for Nitrogen Management)

► Show Figures

Figure 1

11 pages, 2235 KiB

Open AccessArticle

Predicting N Status in Maize with Clip Sensors: Choosing Sensor, Leaf Sampling Point, and Timing

by Jose Luis Gabriel, Miguel Quemada, María Alonso-Ayuso, Jon I. Lizaso and Diana Martín-Lammerding

Sensors 2019, 19(18), 3881; https://doi.org/10.3390/s19183881 - 09 Sep 2019

Cited by 14 | Viewed by 3887

Abstract

Nitrogen (N) losses from agricultural systems increase air and water pollution, and these losses are highly correlated with the excessive fertilization. An adjusted N fertilization is then a key factor in increasing the N fertilizer efficiency, and leaf clip sensors can help to improve it. This study (combining five different field experiments in Central Spain) tried to identify the ability of the clip sensors in maize N status identification and yield prediction, comparing two different devices (SPAD-502^® and Dualex^®) and identifying the best protocol for maize leaf sampling. As a result, the study demonstrated that different leaf clip chlorophyll sensors presented similar results, although some differences appeared at larger N concentrations. Complementary polyphenol information (as flavonol) can improve the maize N deficiency prediction. Moreover, valuable information for a proper sampling protocol was obtained with this study. It proved that the sampling position (in the leaf and in the plant) and sampling time were crucial for a better estimation of the maize N status. Proper fertilization recommendations could be achieved based on clip chlorophyll sensor measurements. Full article

(This article belongs to the Special Issue Proximal Sensing for Nitrogen Management)

► Show Figures

Figure 1

17 pages, 3971 KiB

Open AccessArticle

Sensitivity of Vegetation Indices for Estimating Vegetative N Status in Winter Wheat

by Lukas Prey and Urs Schmidhalter

Sensors 2019, 19(17), 3712; https://doi.org/10.3390/s19173712 - 27 Aug 2019

Cited by 25 | Viewed by 3265

Abstract

Precise sensor-based non-destructive estimation of crop nitrogen (N) status is essential for low-cost, objective optimization of N fertilization, as well as for early estimation of yield potential and N use efficiency. Several studies assessed the performance of spectral vegetation indices (SVI) for winter wheat (Triticum aestivum L.), often either for conditions of low N status or across a wide range of the target traits N uptake (Nup), N concentration (NC), dry matter biomass (DM), and N nutrition index (NNI). This study aimed at a critical assessment of the estimation ability depending on the level of the target traits. It included seven years’ data with nine measurement dates from early stem elongation until flowering in eight N regimes (0–420 kg N ha⁻¹) for selected SVIs. Tested across years, a pronounced date-specific clustering was found particularly for DM and NC. While for DM, only the R900_970 gave moderate but saturated relationships (R² = 0.47, p < 0.001) and no index was useful for NC across dates, NNI and Nup could be better estimated (REIP: R² = 0.59, p < 0.001 for both traits). Tested within growth stages across N levels, the order of the estimation of the traits was mostly Nup ≈ NNI > NC ≈ DM. Depending on the number (n = 1–3) and characteristic of cultivars included, the relationships improved when testing within instead of across cultivars, with the relatively lowest cultivar effect on the estimation of DM and the strongest on NC. For assessing the trait estimation under conditions of high–excessive N fertilization, the range of the target traits was divided into two intervals with NNI values < 0.8 (interval 1: low N status) and with NNI values > 0.8 (interval 2: high N status). Although better estimations were found in interval 1, useful relationships were also obtained in interval 2 from the best indices (DM: R780_740: average R² = 0.35, RMSE = 567 kg ha⁻¹; NC: REIP: average R² = 0.40, RMSE = 0.25%; NNI: REIP: average R² = 0.46, RMSE = 0.10; Nup: REIP: average R² = 0.48, RMSE = 21 kg N ha⁻¹). While in interval 1, all indices performed rather similarly, the three red edge-based indices were clearly better suited for the three N-related traits. The results are promising for applying SVIs also under conditions of high N status, aiming at detecting and avoiding excessive N use. While in canopies of lower N status, the use of simple NIR/VIS indices may be sufficient without losing much precision, the red edge information appears crucial for conditions of higher N status. These findings can be transferred to the configuration and use of simpler multispectral sensors under conditions of contrasting N status in precision farming. Full article

(This article belongs to the Special Issue Proximal Sensing for Nitrogen Management)

► Show Figures

Figure 1

20 pages, 2191 KiB

Open AccessArticle

The Use of Chlorophyll Meters to Assess Crop N Status and Derivation of Sufficiency Values for Sweet Pepper

by Romina de Souza, M. Teresa Peña-Fleitas, Rodney B. Thompson, Marisa Gallardo, Rafael Grasso and Francisco M. Padilla

Sensors 2019, 19(13), 2949; https://doi.org/10.3390/s19132949 - 04 Jul 2019

Cited by 17 | Viewed by 3554

Abstract

Chlorophyll meters are promising tools for improving the nitrogen (N) management of vegetable crops. To facilitate on-farm use of these meters, sufficiency values that identify deficient and sufficient crop N status are required. This work evaluated the ability of three chlorophyll meters (SPAD-502, atLEAF+, and MC-100) to assess crop N status in sweet pepper. It also determined sufficiency values for optimal N nutrition for each meter for pepper. The experimental work was conducted in a greenhouse, in Almería, Spain, very similar to those used for commercial production, in three different crops grown with fertigation. In each crop, there were five treatments of different N concentration in the nutrient solution, applied in each irrigation, ranging from a very deficient to very excessive N supply. In general, chlorophyll meter measurements were strongly related to crop N status in all phenological stages of the three crops, indicating that these measurements are good indicators of the crop N status of pepper. Sufficiency values determined for each meter for the four major phenological stages were consistent between the three crops. This demonstrated the potential for using these meters with sufficiency values to improve the N management of commercial sweet pepper crops. Full article

(This article belongs to the Special Issue Proximal Sensing for Nitrogen Management)

► Show Figures

Figure 1

Review

Jump to: Research

21 pages, 904 KiB

Open AccessReview

Using Hand-Held Chlorophyll Meters and Canopy Reflectance Sensors for Fertilizer Nitrogen Management in Cereals in Small Farms in Developing Countries

by Bijay-Singh and Ali M. Ali

Sensors 2020, 20(4), 1127; https://doi.org/10.3390/s20041127 - 19 Feb 2020

Cited by 47 | Viewed by 5199

Abstract

To produce enough food, smallholder farmers in developing countries apply fertilizer nitrogen (N) to cereals, sometimes even more than the local recommendations. During the last two decades, hand-held chlorophyll meters and canopy reflectance sensors, which can detect the N needs of the crop based on transmission and reflectance properties of leaves through proximal sensing, have been studied as tools for optimizing crop N status in cereals in developing countries. This review aims to describe the outcome of these studies. Chlorophyll meters are used to manage fertilizer N to maintain a threshold leaf chlorophyll content throughout the cropping season. Despite greater reliability of the sufficiency index approach, the fixed threshold chlorophyll content approach has been investigated more for using chlorophyll meters in rice and wheat. GreenSeeker and Crop Circle crop reflectance sensors take into account both N status and biomass of the crop to estimate additional fertilizer N requirement but only a few studies have been carried out in developing countries to develop N management strategies in rice, wheat and maize. Both chlorophyll meters and canopy reflectance sensors can increase fertilizer N use efficiency by reduction of N rates. Dedicated economic analysis of the proximal sensing strategies for managing fertilizer N in cereals in developing countries is not adequately available. Full article

(This article belongs to the Special Issue Proximal Sensing for Nitrogen Management)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Proximal Sensing for Nitrogen Management

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (6 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI