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Sensor-Based Crop and Soil Monitoring in Precise Agriculture
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Sensor-Based Crop and Soil Monitoring in Precise Agriculture

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 5901

Special Issue Editor

Dr. Carlos Campillo

E-Mail Website
Guest Editor
Centre for Scientific and Technological Research of Extremadura (CICYTEX), Department of Horticulture, Finca La Orden, Regional Government of Extremadura, Highway A-V, Km 372, Guadajira, 06187 Badajoz, Spain
Interests: water use efficiency; precision fertilization and irrigation; digital agriculture; remote sensing; crop and soil monitoring; crop and soil modelling; irrigation and fertilization scheduling; automatic irrigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The arrival of ICT technologies in agriculture has opened a new window of opportunity for capturing information about the plant, the crop, and its environment, as well as for managing this information and interpreting it. Agriculture faces a great number of challenges such as climate change, food shortages, innocuousness factors, efficiency in food distribution, and the growth of the world’s population, of which the impact of these factors can be mitigated or reduced with the use of sensors that can help to generate conditions for the optimal growth and development of crops and plants.

Will this technological revolution open the door to new agriculture, or have expectations been created that are still far from being realized? Scientific research must lay the foundation and offer contrasting information regarding which kind of technological progress is best to support new agricultural practices.

This Special Issue aims to provide a scientific link that promotes the exchange of knowledge related to the use of sensors to integrate technology in precision agriculture. The scope includes, but is not limited to, the following topics:

(1) plant-based sensing for biotic and abiotic stress monitoring;
(2) plant and soil moisture sensors for irrigation management;
(3) monitoring UAV and satellite to precision crop and soil management;
(4) using sensors to automate fertilization and irrigation scheduling;
(5) wireless sensor networks for crop and soil management;
(6) assimilation of soil sensor data with models;
(7) soil moisture sensor networks and IoT;
(8) variable-rate fertilization and irrigation;
(9) decision-support systems combined with sensors;
(10) sensory systems for the detection of pests and diseases;
(11) sensors to delineate management zones;
(12) non-contact sensors;
(13) managing soil and plant spatial variability.

Dr. Carlos Campillo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All 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

  • soil moisture
  • irrigation management
  • crop monitoring
  • Internet of Things
  • spatial variability
  • precision agriculture
  • monitoring UAV and satellite
  • decision support system

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Published Papers (5 papers)

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Research

20 pages, 4515 KiB  
Article
Evaluation of Different Commercial Sensors for the Development of Their Automatic Irrigation System
by Sandra Millán, Cristina Montesinos and Carlos Campillo
Sensors 2024, 24(23), 7468; https://doi.org/10.3390/s24237468 - 22 Nov 2024
Abstract
Reliable soil moisture information is essential for accurate irrigation scheduling. A wide range of soil moisture sensors are currently available on the market, but their performance needs to be evaluated as most sensors are calibrated under limited laboratory conditions. The aim of this [...] Read more.
Reliable soil moisture information is essential for accurate irrigation scheduling. A wide range of soil moisture sensors are currently available on the market, but their performance needs to be evaluated as most sensors are calibrated under limited laboratory conditions. The aim of this study was to evaluate the performance of six commercially available moisture sensors (HydraProbe, Teros 10, Teros 11, EnviroPro, CS616 and Drill & Drop) and three tensiometers (Irrometer RSU-C-34, Teros 32 and Teros 21) and to establish calibration equations for a typical sandy soil of the Doñana National Park (Huelva, Spain). The calibration process for soil moisture sensors indicated differences between factory and corrected equations. All tested sensors improved with adjustments made to the factory calibration, except for the HydraProbe sensor which had a more accurate factory equation for a sandy soil. Among the various sensors tested, the Teros 10, Teros 11, and HydraProbe were found to be the easiest to install, typically positioned with an auger to prevent preferential pathways and ensure adequate sensor-soil contact. For tensiometers, the Teros 32 sensor requires specialized labor for its correct installation, as it must be positioned at a specific angle and maintained with distilled water. All tensiometers need a stabilization period after installation. Full article
(This article belongs to the Special Issue Sensor-Based Crop and Soil Monitoring in Precise Agriculture)
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13 pages, 3430 KiB  
Article
Assessment of Low-Cost and Higher-End Soil Moisture Sensors across Various Moisture Ranges and Soil Textures
by Rajesh Nandi and Dev Shrestha
Sensors 2024, 24(18), 5886; https://doi.org/10.3390/s24185886 - 11 Sep 2024
Viewed by 1552
Abstract
The accuracy and unit cost of sensors are important factors for a continuous soil moisture monitoring system. This study compares the accuracy of four soil moisture sensors differing in unit costs in coarse-, fine-, and medium-textured soils. The sensor outputs were recorded for [...] Read more.
The accuracy and unit cost of sensors are important factors for a continuous soil moisture monitoring system. This study compares the accuracy of four soil moisture sensors differing in unit costs in coarse-, fine-, and medium-textured soils. The sensor outputs were recorded for the VWC, ranging from 0% to 50%. Low-cost capacitive and resistive sensors were evaluated with and without the external 16-bit analog-to-digital converter ADS1115 to improve their performances without adding much cost. Without ADS1115, using only Arduino’s built-in analog-to-digital converter, the low-cost sensors had a maximum RMSE of 4.79% (v/v) for resistive sensors and 3.78% for capacitive sensors in medium-textured soil. The addition of ADS1115 showed improved performance of the low-cost sensors, with a maximum RMSE of 2.64% for resistive sensors and 1.87% for capacitive sensors. The higher-end sensors had an RMSE of up to 1.8% for VH400 and up to 0.95% for the 5TM sensor. The RMSE differences between higher-end and low-cost sensors with the use of ADS1115 were not statistically significant. Full article
(This article belongs to the Special Issue Sensor-Based Crop and Soil Monitoring in Precise Agriculture)
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12 pages, 2983 KiB  
Article
Precise Positioning in Nitrogen Fertility Sensing in Maize (Zea mays L.)
by Tri Setiyono
Sensors 2024, 24(16), 5322; https://doi.org/10.3390/s24165322 - 17 Aug 2024
Viewed by 780
Abstract
This study documented the contribution of precise positioning involving a global navigation satellite system (GNSS) and a real-time kinematic (RTK) system in unmanned aerial vehicle (UAV) photogrammetry, particularly for establishing the coordinate data of ground control points (GCPs). Without augmentation, GNSS positioning solutions [...] Read more.
This study documented the contribution of precise positioning involving a global navigation satellite system (GNSS) and a real-time kinematic (RTK) system in unmanned aerial vehicle (UAV) photogrammetry, particularly for establishing the coordinate data of ground control points (GCPs). Without augmentation, GNSS positioning solutions are inaccurate and pose a high degree of uncertainty if such data are used in UAV data processing for mapping. The evaluation included a comparative assessment of sample coordinates involving RTK and an ordinary GPS device and the application of precise GCP data for UAV photogrammetry in field crop research, monitoring nitrogen deficiency stress in maize. This study confirmed the superior performance of the RTK system in providing positional data, with 4 cm bias as compared to 311 cm with the non-augmented GNSS technique, making it suitable for use in agronomic research involving row crops. Precise GCP data in this study allow the UAV-based Normalized Difference Red-Edge Index (NDRE) data to effectively characterize maize crop responses to N nutrition during the growing season, with detailed analyses revealing the causal relationship in that a compromised optimum canopy chlorophyll content under limiting nitrogen environment was the reason for reduced canopy cover under an N-deficiency environment. Without RTK-based GCPs, different and, to some degree, misleading results were evident, and therefore, this study warrants the requirement of precise GCP data for scientific research investigations attempting to use UAV photogrammetry for agronomic field crop study. Full article
(This article belongs to the Special Issue Sensor-Based Crop and Soil Monitoring in Precise Agriculture)
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24 pages, 7503 KiB  
Article
Spatial and Spectral Dependencies of Maize Yield Estimation Using Remote Sensing
by Nathan Burglewski, Subhashree Srinivasagan, Quirine Ketterings and Jan van Aardt
Sensors 2024, 24(12), 3958; https://doi.org/10.3390/s24123958 - 18 Jun 2024
Viewed by 1374
Abstract
Corn (Zea mays L.) is the most abundant food/feed crop, making accurate yield estimation a critical data point for monitoring global food production. Sensors with varying spatial/spectral configurations have been used to develop corn yield models from intra-field (0.1 m ground sample [...] Read more.
Corn (Zea mays L.) is the most abundant food/feed crop, making accurate yield estimation a critical data point for monitoring global food production. Sensors with varying spatial/spectral configurations have been used to develop corn yield models from intra-field (0.1 m ground sample distance (GSD)) to regional scales (>250 m GSD). Understanding the spatial and spectral dependencies of these models is imperative to result interpretation, scaling, and deploying models. We leveraged high spatial resolution hyperspectral data collected with an unmanned aerial system mounted sensor (272 spectral bands from 0.4–1 μm at 0.063 m GSD) to estimate silage yield. We subjected our imagery to three band selection algorithms to quantitatively assess spectral reflectance features applicability to yield estimation. We then derived 11 spectral configurations, which were spatially resampled to multiple GSDs, and applied to a support vector regression (SVR) yield estimation model. Results indicate that accuracy degrades above 4 m GSD across all configurations, and a seven-band multispectral sensor which samples the red edge and multiple near-infrared bands resulted in higher accuracy in 90% of regression trials. These results bode well for our quest toward a definitive sensor definition for global corn yield modeling, with only temporal dependencies requiring additional investigation. Full article
(This article belongs to the Special Issue Sensor-Based Crop and Soil Monitoring in Precise Agriculture)
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18 pages, 5812 KiB  
Article
Design of an Ultrasound Sensing System for Estimation of the Porosity of Agricultural Soils
by Stuart Bradley and Chandra Ghimire
Sensors 2024, 24(7), 2266; https://doi.org/10.3390/s24072266 - 2 Apr 2024
Cited by 1 | Viewed by 1417
Abstract
The design of a readily useable technology for routine paddock-scale soil porosity estimation is described. The method is non-contact (proximal) and typically from “on-the-go” sensors mounted on a small farm vehicle around 1 m above the soil surface. This ultrasonic sensing method is [...] Read more.
The design of a readily useable technology for routine paddock-scale soil porosity estimation is described. The method is non-contact (proximal) and typically from “on-the-go” sensors mounted on a small farm vehicle around 1 m above the soil surface. This ultrasonic sensing method is unique in providing estimates of porosity by a non-invasive, cost-effective, and relatively simple method. Challenges arise from the need to have a compact low-power rigid structure and to allow for pasture cover and surface roughness. The high-frequency regime for acoustic reflections from a porous material is a function of the porosity ϕ, the tortuosity α∞, and the angle of incidence θ. There is no dependence on frequency, so measurements must be conducted at two or more angles of incidence θ to obtain two or more equations in the unknown soil properties ϕ and α∞. Sensing and correcting for scattering of ultrasound from a rough soil surface requires measurements at three or more angles of incidence. A system requiring a single transmitter/receiver pair to be moved from one angle to another is not viable for rapid sampling. Therefore, the design includes at least three transmitter/reflector pairs placed at identical distances from the ground so that they would respond identically to power reflected from a perfectly reflecting surface. A single 25 kHz frequency is a compromise which allows for the frequency-dependent signal loss from a natural rough agricultural soil surface. Multiple-transmitter and multiple-microphone arrays are described which give a good signal-to-noise ratio while maintaining a compact system design. The resulting arrays have a diameter of 100 mm. Pulsed ultrasound is used so that the reflected sound can be separated from sound travelling directly through the air horizontally from transmitter to receiver. The average porosity estimated for soil samples in the laboratory and in the field is found to be within around 0.04 of the porosity measured independently. This level of variation is consistent with uncertainties in setting the angle of incidence, although assumptions made in modelling the interaction of ultrasound with the rough surface no doubt also contribute. Although the method is applicable to all soil types, the current design has only been tested on dry, vegetation-free soils for which the sampled area does not contain large animal footprints or rocks. Full article
(This article belongs to the Special Issue Sensor-Based Crop and Soil Monitoring in Precise Agriculture)
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