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Sensor-Based Crop and Soil Monitoring in Precise Agriculture

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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 16977

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Dr. Carlos Campillo

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

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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 (10 papers)

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Research

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23 pages, 2994 KiB

Open AccessArticle

Effects of Sensor-Based, Site-Specific Nitrogen Fertilizer Application on Crop Yield, Nitrogen Balance, and Nitrogen Efficiency

by Ludwig Hagn, Martin Mittermayer, Andreas Kern, Stefan Kimmelmann, Franz-Xaver Maidl and Kurt-Jürgen Hülsbergen

Sensors 2025, 25(3), 795; https://doi.org/10.3390/s25030795 - 28 Jan 2025

Cited by 1 | Viewed by 746

Abstract

This study investigates the effects of sensor-based, variable-rate mineral nitrogen (N) application (VRA) in winter wheat (Triticum aestivum L.) on the spatial variability of grain yield, protein content, N uptake, N balance, and N efficiency compared with uniform N application (UA). To analyze the effects of VRA and UA on yield and N balance parameters, on-farm strip trials were conducted on heterogeneous arable fields covering an area of 49 hectares. The trials were carried out over a four-year period, from 2020 to 2023, with crops under both application methods placed in strips side-by-side. The N fertilizer requirements for growth stages (GSs) 32 and 39 were determined using an online map-overlay VRA method. This method integrated the site-specific yield potential and current plant development derived from spectral reflectance measurements using a tractor-mounted sensor system. The results show that the application of N fertilizer can be reduced by up to 38 kg ha⁻¹ yr⁻¹. The N efficiency can be increased by 15% and a significant reduction in variability of N balances can be achieved. However, the effects on yield and N efficiency are highly dependent on the specific application conditions (weather conditions, disease occurrence, and crop development). Not every field trial showed advantages of VRA over UA fertilization. Overall, the VRA system demonstrated encouraging potential, functioning as intended. However, further adjustment and optimization are required to ensure that the VRA fertilization system works robustly and reliably under on-farm conditions. Full article

(This article belongs to the Special Issue Sensor-Based Crop and Soil Monitoring in Precise Agriculture)

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22 pages, 2303 KiB

Open AccessArticle

Real-Time Classification of Ochratoxin a Contamination in Grapes Using AI-Enhanced IoT

by Mohamed Riad Sebti, Zohra Dakhia, Sonia Carabetta, Rosa Di Sanzo, Mariateresa Russo and Massimo Merenda

Sensors 2025, 25(3), 784; https://doi.org/10.3390/s25030784 - 28 Jan 2025

Cited by 1 | Viewed by 609

Abstract

Ochratoxin A (OTA) contamination presents significant risks in viticulture, affecting the safety and quality of wine and grape-derived products. This study introduces a groundbreaking method for early detection and management of OTA, leveraging environmental data such as temperature and humidity. A function derived from chemical analysis was developed to estimate OTA concentrations and used to label a synthetic dataset, establishing safe thresholds. Two AI models were trained: one for the detecting of OTA presence and the other for classifying the concentration range. These models were deployed on a M5Stick C+, a microcontroller designed for real-time data processing. The inference process is optimized for rapid response, requiring minimal time to deliver results. Additionally, the low power consumption of the M5Stick C+ ensures that the device can operate throughout the harvest period on a single charge. The system is able to transmit inference data via MQTT for real-time analysis. This comprehensive approach offers a scalable, cost-effective, on-site solution that is autonomous, eliminating the need for domain experts and extensive resources. The robustness of the system was demonstrated through its consistent performance across multiple test sets, providing an effective enhancement to food safety in grape and wine production. The study also details the system architecture, describes the function used for data labeling, outlines the training and deployment processes of the models, and finally, assesses the testing of the overall system. Full article

(This article belongs to the Special Issue Sensor-Based Crop and Soil Monitoring in Precise Agriculture)

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28 pages, 1231 KiB

Open AccessArticle

Improving the Calibration of Low-Cost Sensors Using Data Assimilation

by Diego Alberto Aranda Britez, Alejandro Tapia Córdoba, Princy Johnson, Erid Eulogio Pacheco Viana and Pablo Millán Gata

Sensors 2024, 24(23), 7846; https://doi.org/10.3390/s24237846 - 8 Dec 2024

Viewed by 1012

Abstract

In the context of smart agriculture, accurate soil moisture monitoring is crucial to optimise irrigation, improve water usage efficiency and increase crop yields. Although low-cost capacitive sensors are used to make monitoring affordable, these sensors face accuracy challenges that often result in inefficient irrigation practices. This paper presents a method for calibrating capacitive soil moisture sensors through data assimilation. The method was validated using data collected from a farm in Dos Hermanas, Seville, Spain, which utilises a drip irrigation system. The proposed solution integrates the Hydrus 1D model with particle filter (PF) and the Iterative Ensemble Smoother (IES) to continuously update and refine the model and sensor calibration parameters. The methodology includes the implementation of physical constraints, ensuring that the updated parameters remain within physically plausible ranges. Soil moisture was measured using low-cost SoilWatch 10 capacitive sensors and ThetaProbe ML3 high-precision sensors as a reference. Furthermore, a comparison was carried out between the PF and IES methods. The results demonstrate that the data assimilation approach markedly enhances the precision of sensor readings, aligning them closely with reference measurements and model simulations. The PF method demonstrated superior performance, achieving an 84.8% improvement in accuracy compared to the raw sensor readings. This substantial improvement was measured against high-precision reference sensors, confirming the effectiveness of the PF method in calibrating low-cost capacitive sensors. In contrast, the IES method showed a 68% improvement in accuracy, which, while still considerable, was outperformed by the PF. By effectively mitigating observation noise and sensor biases, this approach proves robust and practical for large-scale implementations in precision agriculture. Full article

(This article belongs to the Special Issue Sensor-Based Crop and Soil Monitoring in Precise Agriculture)

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13 pages, 3702 KiB

Open AccessArticle

Soil Sensor Use in Delimiting Management Zones for Sowing Maize in No-Till

by Eduardo Leonel Bottega, Ederson Bitencourt Pinto, Ezequiel Saretta, Zanandra Boff de Oliveira, Filipe Silveira Severo and Johan Assmann

Sensors 2024, 24(23), 7552; https://doi.org/10.3390/s24237552 - 26 Nov 2024

Viewed by 623

Abstract

This study aimed to analyze yield components and maize yield cultivated at different population densities in management zones (MZs) delimited based on mapping the spatial variability of the soil’s apparent electrical conductivity (ECa). The soil ECa was measured, and two MZs were subsequently delimited, one with low ECa and the other with high ECa. In each MZ, four maize sowing densities were tested: 60,000 (D1); 80,000 (D2); 100,000 (D3); and 140,000 (D4) seeds ha⁻¹. Ear length, number of grains per ear, number of grains per row, number of rows per ear, thousand-grain weight, and yield were evaluated. The increase in sowing density in the high ECa MZ linearly reduced the values of ear diameter, number of rows per ear, number of grains per ear, and thousand-grain weight. Sowing density D3, when implemented in the low ECa MZ, showed higher values for the ear length, ear diameter, number of grains per row, number of grains per ear, and thousand-grain weight. Sowing density D2 was the one with the highest yield, regardless of the MZ where it was implemented (5628.48 kg ha⁻¹ in the high ECa management zone and 4463.63 kg ha⁻¹ in the low ECa). Full article

(This article belongs to the Special Issue Sensor-Based Crop and Soil Monitoring in Precise Agriculture)

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20 pages, 4515 KiB

Open AccessArticle

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

Cited by 3 | Viewed by 1166

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 4140

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 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

Open AccessEditor’s ChoiceArticle

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

Cited by 2 | Viewed by 1345

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1839

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 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

Open AccessArticle

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 2157

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 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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Review

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39 pages, 6290 KiB

Open AccessReview

Trends of Soil and Solution Nutrient Sensing for Open Field and Hydroponic Cultivation in Facilitated Smart Agriculture

by Md Nasim Reza, Kyu-Ho Lee, Md Rejaul Karim, Md Asrakul Haque, Emmanuel Bicamumakuba, Pabel Kanti Dey, Young Yoon Jang and Sun-Ok Chung

Sensors 2025, 25(2), 453; https://doi.org/10.3390/s25020453 - 14 Jan 2025

Cited by 2 | Viewed by 2918

Abstract

Efficient management of soil nutrients is essential for optimizing crop production, ensuring sustainable agricultural practices, and addressing the challenges posed by population growth and environmental degradation. Smart agriculture, using advanced technologies, plays an important role in achieving these goals by enabling real-time monitoring and precision management of nutrients. In open-field soil cultivation, spatial variability in soil properties demands site-specific nutrient management and integration with variable-rate technology (VRT) to optimize fertilizer application, reduce nutrient losses, and enhance crop yields. Hydroponic solution cultivation, on the other hand, requires precise monitoring and control of nutrient solutions to maintain optimal conditions for plant growth, ensuring efficient use of water and fertilizers. This review aims to explore recent trends in soil and solution nutrient sensing technologies for open-field soil and facilitated hydroponic cultivation, highlighting advancements that promote efficiency and sustainability. Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. Blockchain technology is also emerging as a tool to enhance transparency and traceability in nutrient management, promoting compliance with environmental standards and sustainable practices. In open-field soil cultivation, real-time sensing technologies support targeted nutrient application by accounting for spatial variability, minimizing environmental risks such as runoff and eutrophication. In hydroponic solution cultivation, precise solution sensing ensures nutrient balance, optimizing plant health and productivity. By advancing these technologies, smart agriculture can achieve sustainable crop production, improved resource efficiency, and environmental protection, fostering a resilient food system. Full article

(This article belongs to the Special Issue Sensor-Based Crop and Soil Monitoring in Precise Agriculture)

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