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Sensors and Systems for Smart 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 (31 October 2019) | Viewed by 71545

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Dr. Daniela Stroppiana

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Institute for Electromagnetic Sensing of the Environment, Italian National Research Council, (IREA-CNR), 7-00185 Roma, Italy
Interests: burned area mapping; multi-spectral image processing; time series analysis; assessment of fire impacts
Special Issues, Collections and Topics in MDPI journals

Dr. Mirco Boschetti

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Institute for the Electromagnetic Sensing of Environment, National Research Council, Via Corti 12, 20133 Milan, Italy
Interests: optical remote sensing; imaging spectroscopy; vegetation properties retrieval; radiative transfer models; agricultural monitoring and digital application
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Special Issue Information

Dear Colleagues,

International organisations, such as FAO, state that producing more food with less natural resources is a challenge of the future, due to the foreseen increase in global population that is expected to exceed nine billion by 2040, as well as the effects of climate change. Great expectation is in digital technologies recognised as an efficient tool to provide key data stream to support both smart and sustainable crop management and phenotyping for plant breeding.

A large number of sensors are available, as are methodological and technical solutions to collect measurements, store and integrate data and extract added value information to be ingested in operational monitoring and managing systems.

This Special Issue will collect contributions on available sensors for agriculture, soil and plant monitoring and processing techniques with a particular interest in new sensors and frontier applications in precision farming and phenotyping sectors. In this framework, contributions presenting operational workflows based on sensors, advanced data processing techniques and their integration in Decision Support Systems (DSS) are encouraged.

Dr. Daniela Stroppiana
Dr. Mirco Boschetti
Guest Editors

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Keywords

Multi-spectral, hyper-spectral and active radar and LiDAR sensors
Space-borne, air-borne and UAV platforms
Proximal sensing and robotic manned and unmanned systems
Advanced in-plant sensors
Algorithm to automatically derive soil and plant properties
Accuracy of sensor measurements and parameters estimates
Smart farming, precision agriculture, and phenotyping
Smart applications for site-specific crop monitoring and management
Data processing techniques and related big data problem and solution
IoT solutions and automation
Decision support systems and making (AI, machine learning)

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Published Papers (10 papers)

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Research

15 pages, 1836 KiB

Open AccessArticle

Proposal for an Embedded System Architecture Using a GNDVI Algorithm to Support UAV-Based Agrochemical Spraying

by Maik Basso, Diego Stocchero, Renato Ventura Bayan Henriques, André Luis Vian, Christian Bredemeier, Andréa Aparecida Konzen and Edison Pignaton de Freitas

Sensors 2019, 19(24), 5397; https://doi.org/10.3390/s19245397 - 7 Dec 2019

Cited by 29 | Viewed by 6987

Abstract

An important area in precision agriculture is related to the efficient use of chemicals applied onto fields. Efforts have been made to diminish their use, aiming at cost reduction and fewer chemical residues in the final agricultural products. The use of unmanned aerial vehicles (UAVs) presents itself as an attractive and cheap alternative for spraying pesticides and fertilizers compared to conventional mass spraying performed by ordinary manned aircraft. Besides being cheaper than manned aircraft, small UAVs are capable of performing fine-grained instead of the mass spraying. Observing this improved method, this paper reports the design of an embedded real-time UAV spraying control system supported by onboard image processing. The proposal uses a normalized difference vegetation index (NDVI) algorithm to detect the exact locations in which the chemicals are needed. Using this information, the automated spraying control system performs punctual applications while the UAV navigates over the crops. The system architecture is designed to run on low-cost hardware, which demands an efficient NDVI algorithm. The experiments were conducted using Raspberry Pi 3 as the embedded hardware. First, experiments in a laboratory were conducted in which the algorithm was proved to be correct and efficient. Then, field tests in real conditions were conducted for validation purposes. These validation tests were performed in an agronomic research station with the Raspberry hardware integrated into a UAV flying over a field of crops. The average CPU usage was about 20% while memory consumption was about 70 MB for high definition images, with 4% CPU usage and 20.3 MB RAM being observed for low-resolution images. The average current measured to execute the proposed algorithm was 0.11 A. The obtained results prove that the proposed solution is efficient in terms of processing and energy consumption when used in embedded hardware and provides measurements which are coherent with the commercial GreenSeeker equipment. Full article

(This article belongs to the Special Issue Sensors and Systems for Smart Agriculture)

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13 pages, 3552 KiB

Open AccessArticle

Development of an In Vivo Sensor to Monitor the Effects of Vapour Pressure Deficit (VPD) Changes to Improve Water Productivity in Agriculture

by Filippo Vurro, Michela Janni, Nicola Coppedè, Francesco Gentile, Riccardo Manfredi, Manuele Bettelli and Andrea Zappettini

Sensors 2019, 19(21), 4667; https://doi.org/10.3390/s19214667 - 28 Oct 2019

Cited by 39 | Viewed by 6814

Abstract

Environment, biodiversity and ecosystem services are essential to ensure food security and nutrition. Managing natural resources and mainstreaming biodiversity across agriculture sectors are keys towards a sustainable agriculture focused on resource efficiency. Vapour Pressure Deficit (VPD) is considered the main driving force of water movements in the plant vascular system, however the tools available to monitor this parameter are usually based on environmental monitoring. The driving motif of this paper is the development of an in-vivo sensor to monitor the effects of VPD changes in the plant. We have used an in vivo sensor, termed “bioristor”, to continuously monitor the changes occurring in the sap ion’s status when plants experience different VPD conditions and we observed a specific R (sensor response) trend in response to VPD. The possibility to directly monitor the physiological changes occurring in the plant in different VPD conditions, can be used to increase efficiency of the water management in controlled conditions thus achieving a more sustainable use of natural resources. Full article

(This article belongs to the Special Issue Sensors and Systems for Smart Agriculture)

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11 pages, 1768 KiB

Open AccessArticle

Capturing Maize Stand Heterogeneity Across Yield-Stability Zones Using Unmanned Aerial Vehicles (UAV)

by Guanyuan Shuai, Rafael A. Martinez-Feria, Jinshui Zhang, Shiming Li, Richard Price and Bruno Basso

Sensors 2019, 19(20), 4446; https://doi.org/10.3390/s19204446 - 14 Oct 2019

Cited by 18 | Viewed by 4932

Abstract

Despite the new equipment capabilities, uneven crop stands are still common occurrences in crop fields, mainly due to spatial heterogeneity in soil conditions, seedling mortality due to herbivore predation and disease, or human error. Non-uniform plant stands may reduce grain yield in crops like maize. Thus, detecting signs of variability in crop stand density early in the season provides critical information for management decisions and crop yield forecasts. Processing techniques applied on images captured by unmanned aerial vehicles (UAVs) has been used successfully to identify crop rows and estimate stand density and, most recently, to estimate plant-to-plant interval distance. Here, we further test and apply an image processing algorithm on UAV images collected from yield-stability zones in a commercial crop field. Our objective was to implement the algorithm to compare variation of plant-spacing intervals to test whether yield differences within these zones are related to differences in crop stand characteristics. Our analysis indicates that the algorithm can be reliably used to estimate plant counts (precision >95% and recall >97%) and plant distance interval (R² ~0.9 and relative error <10%). Analysis of the collected data indicated that plant spacing variability differences were small among plots with large yield differences, suggesting that it was not a major cause of yield variability across zones with distinct yield history. This analysis provides an example of how plant-detection algorithms can be applied to improve the understanding of patterns of spatial and temporal yield variability. Full article

(This article belongs to the Special Issue Sensors and Systems for Smart Agriculture)

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22 pages, 18848 KiB

Open AccessArticle

Integrating Geophysical and Multispectral Data to Delineate Homogeneous Management Zones within a Vineyard in Northern Italy

by Bianca Ortuani, Giovanna Sona, Giulia Ronchetti, Alice Mayer and Arianna Facchi

Sensors 2019, 19(18), 3974; https://doi.org/10.3390/s19183974 - 14 Sep 2019

Cited by 10 | Viewed by 3336

Abstract

Soil electrical conductivity (EC) maps obtained through proximal soil sensing (i.e., geophysical data) are usually considered to delineate homogeneous site-specific management zones (SSMZ), used in Precision Agriculture to improve crop production. The recent literature recommends the integration of geophysical soil monitoring data with crop information acquired through multispectral (VIS-NIR) imagery. In non-flat areas, where topography can influence the soil water conditions and consequently the crop water status and the crop yield, considering topography data together with soil and crop data may improve the SSMZ delineation. The objective of this study was the fusion of EC and VIS-NIR data to delineate SSMZs in a rain-fed vineyard located in Northern Italy (Franciacorta), and the assessment of the obtained SSMZ map through the comparison with data acquired by a thermal infrared (TIR) survey carried out during a hot and dry period of the 2017 agricultural season. Data integration is performed by applying multivariate statistical methods (i.e., Principal Component Analysis). The results show that the combined use of soil, topography and crop information improves the SSMZ delineation. Indeed, the correspondence between the SSMZ map and the CWSI map derived from TIR imagery was enhanced by including the NDVI information. Full article

(This article belongs to the Special Issue Sensors and Systems for Smart Agriculture)

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20 pages, 17999 KiB

Open AccessArticle

Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images

by Christoffer Bøgelund Rasmussen and Thomas B. Moeslund

Sensors 2019, 19(16), 3506; https://doi.org/10.3390/s19163506 - 10 Aug 2019

Cited by 9 | Viewed by 4581

Abstract

Efficient and robust evaluation of kernel processing from corn silage is an important indicator to a farmer to determine the quality of their harvested crop. Current methods are cumbersome to conduct and take between hours to days. We present the adoption of two deep learning-based methods for kernel processing prediction without the cumbersome step of separating kernels and stover before capturing images. The methods show that kernels can be detected both with bounding boxes and at pixel-level instance segmentation. Networks were trained on up to 1393 images containing just over 6907 manually annotated kernel instances. Both methods showed promising results despite the challenging setting, with an average precision at an intersection-over-union of 0.5 of 34.0% and 36.1% on the test set consisting of images from three different harvest seasons for the bounding-box and instance segmentation networks respectively. Additionally, analysis of the correlation between the Kernel Processing Score (KPS) of annotations against the KPS of model predictions showed a strong correlation, with the best performing at r(15) = 0.88, p = 0.00003. The adoption of deep learning-based object recognition approaches for kernel processing measurement has the potential to lower the quality assessment process to minutes, greatly aiding a farmer in the strenuous harvesting season. Full article

(This article belongs to the Special Issue Sensors and Systems for Smart Agriculture)

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16 pages, 3874 KiB

Open AccessArticle

Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform

by Chengquan Zhou, Hongbao Ye, Jun Hu, Xiaoyan Shi, Shan Hua, Jibo Yue, Zhifu Xu and Guijun Yang

Sensors 2019, 19(14), 3106; https://doi.org/10.3390/s19143106 - 13 Jul 2019

Cited by 59 | Viewed by 5350

Abstract

The number of panicles per unit area is a common indicator of rice yield and is of great significance to yield estimation, breeding, and phenotype analysis. Traditional counting methods have various drawbacks, such as long delay times and high subjectivity, and they are easily perturbed by noise. To improve the accuracy of rice detection and counting in the field, we developed and implemented a panicle detection and counting system that is based on improved region-based fully convolutional networks, and we use the system to automate rice-phenotype measurements. The field experiments were conducted in target areas to train and test the system and used a rotor light unmanned aerial vehicle equipped with a high-definition RGB camera to collect images. The trained model achieved a precision of 0.868 on a held-out test set, which demonstrates the feasibility of this approach. The algorithm can deal with the irregular edge of the rice panicle, the significantly different appearance between the different varieties and growing periods, the interference due to color overlapping between panicle and leaves, and the variations in illumination intensity and shading effects in the field. The result is more accurate and efficient recognition of rice-panicles, which facilitates rice breeding. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a global scale. Full article

(This article belongs to the Special Issue Sensors and Systems for Smart Agriculture)

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27 pages, 3464 KiB

Open AccessArticle

Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques

by Ma. Luisa Buchaillot, Adrian Gracia-Romero, Omar Vergara-Diaz, Mainassara A. Zaman-Allah, Amsal Tarekegne, Jill E. Cairns, Boddupalli M. Prasanna, Jose Luis Araus and Shawn C. Kefauver

Sensors 2019, 19(8), 1815; https://doi.org/10.3390/s19081815 - 16 Apr 2019

Cited by 63 | Viewed by 7539

Abstract

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R² > 0.60), outperformed other models using only agronomic parameters or field sensors (R² > 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions. Full article

(This article belongs to the Special Issue Sensors and Systems for Smart Agriculture)

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19 pages, 4493 KiB

Open AccessArticle

Estimating Crop Nutritional Status Using Smart Apps to Support Nitrogen Fertilization. A Case Study on Paddy Rice

by Livia Paleari, Ermes Movedi, Fosco M. Vesely, William Thoelke, Sofia Tartarini, Marco Foi, Mirco Boschetti, Francesco Nutini and Roberto Confalonieri

Sensors 2019, 19(4), 981; https://doi.org/10.3390/s19040981 - 25 Feb 2019

Cited by 16 | Viewed by 5411

Abstract

Accurate nitrogen (N) management is crucial for the economic and environmental sustainability of cropping systems. Different methods have been developed to increase the efficiency of N fertilizations. However, their costs and/or low usability have often prevented their adoption in operational contexts. We developed a diagnostic system to support topdressing N fertilization based on the use of smart apps to derive a N nutritional index (NNI; actual/critical plant N content). The system was tested on paddy rice via dedicated field experiments, where the smart apps PocketLAI and PocketN were used to estimate, respectively, critical (from leaf area index) and actual plant N content. Results highlighted the system’s capability to correctly detect the conditions of N stress (NNI < 1) and N surplus (NNI > 1), thereby effectively supporting topdressing fertilizations. A resource-efficient methodology to derive PocketN calibration curves for different varieties—needed to extend the system to new contexts—was also developed and successfully evaluated on 43 widely grown European varieties. The widespread availability of smartphones and the possibility to integrate NNI and remote sensing technologies to derive variable rate fertilization maps generate new opportunities for supporting N management under real farming conditions. Full article

(This article belongs to the Special Issue Sensors and Systems for Smart Agriculture)

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16 pages, 3516 KiB

Open AccessArticle

Design and Calibration of a Low-Cost SDI-12 Soil Moisture Sensor

by Juan D. González-Teruel, Roque Torres-Sánchez, Pedro J. Blaya-Ros, Ana B. Toledo-Moreo, Manuel Jiménez-Buendía and Fulgencio Soto-Valles

Sensors 2019, 19(3), 491; https://doi.org/10.3390/s19030491 - 25 Jan 2019

Cited by 79 | Viewed by 15347

Abstract

Water is the main limiting factor in agricultural production as well as a scarce resource that needs to be optimized. The measurement of soil water with sensors is an efficient way for optimal irrigation management. However, commercial sensors are still too expensive for most farmers. This paper presents the design, development and calibration of a new capacitive low-cost soil moisture sensor that incorporates SDI-12 communication, allowing one to select the calibration equation for different soils. The sensor was calibrated in three different soils and its variability and accuracy were evaluated. Lower but cost-compensated accuracy was observed in comparing it with commercial sensors. Field tests have demonstrated the temperature influence on the sensor and its capability to efficiently detect irrigation and rainfall events. Full article

(This article belongs to the Special Issue Sensors and Systems for Smart Agriculture)

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17 pages, 1950 KiB

Open AccessArticle

Performance Assessment of Five Different Soil Moisture Sensors under Irrigated Field Conditions in Oklahoma

by Sumon Datta, Saleh Taghvaeian, Tyson E. Ochsner, Daniel Moriasi, Prasanna Gowda and Jean L. Steiner

Sensors 2018, 18(11), 3786; https://doi.org/10.3390/s18113786 - 5 Nov 2018

Cited by 50 | Viewed by 9825

Abstract

Meeting the ever-increasing global food, feed, and fiber demands while conserving the quantity and quality of limited agricultural water resources and maintaining the sustainability of irrigated agriculture requires optimizing irrigation management using advanced technologies such as soil moisture sensors. In this study, the performance of five different soil moisture sensors was evaluated for their accuracy in two irrigated cropping systems, one each in central and southwest Oklahoma, with variable levels of soil salinity and clay content. With factory calibrations, three of the sensors had sufficient accuracies at the site with lower levels of salinity and clay, while none of them performed satisfactorily at the site with higher levels of salinity and clay. The study also investigated the performance of different approaches (laboratory, sensor-based, and the Rosetta model) to determine soil moisture thresholds required for irrigation scheduling, i.e., field capacity (FC) and wilting point (WP). The estimated FC and WP by the Rosetta model were closest to the laboratory-measured data using undisturbed soil cores, regardless of the type and number of input parameters used in the Rosetta model. The sensor-based method of ranking the readings resulted in overestimation of FC and WP. Finally, soil moisture depletion, a critical parameter in effective irrigation scheduling, was calculated by combining sensor readings and FC estimates. Ranking-based FC resulted in overestimation of soil moisture depletion, even for accurate sensors at the site with lower levels of salinity and clay. Full article

(This article belongs to the Special Issue Sensors and Systems for Smart Agriculture)

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