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AgriEngineering
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Sensors and Actuators for Crops and Livestock Farming
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Sensors and Actuators for Crops and Livestock Farming

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Sensors Technology and Precision Agriculture".

Deadline for manuscript submissions: closed (30 August 2024) | Viewed by 15976

Special Issue Editors

Dr. Santosh Pandey

E-Mail Website
Guest Editor
Electrical and Computer Engineering, Iowa State University, Ames, IA 50011-1046, USA
Interests: sensors; microfluidics; microelectronics; MEMS; diagnostics; plant pathology; linear actuators; Ag robotics
Dr. Tiago Paim

E-Mail Website
Guest Editor
Federal Institute of Education, Science and Technology Goiano, Campus Rio Verde, Rio Verde 75900-000, GO, Brazil
Interests: sheep genetics; cattle genetics; population genetics; genetic improvement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors and actuators have played an important role in the agricultural revolution of monitoring crops and livestock in an automated and high throughput manner. For example, novel sensors are being developed for irrigation management, nutrient and pesticide application, early disease detection, and environmental monitoring (soil properties, rainfall, and temperature). Similarly, novel actuators are being developed for the agricultural automation of fruit picking, variable sprayers, fertilizer ejectors, ventilation systems, and climate control. The challenge lies in combining a multitude of sensors and actuators into integrated systems to gather real-time farm data and extract critical parameters related to the growth and health of crops and livestock. These data collection tools and techniques are critical for the subsequent construction of reliable expert systems and decision support with the aim of assisting farmers. As such, this Special Issue invites submissions cantered around novel sensors and actuators for agriculture (both crops and livestock farming) and exploring data collection and data management pipelines to effectively capture the intra- and intervariability in farm data with acceptable quality and resolution.

Dr. Santosh Pandey
Dr. Tiago Paim
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. AgriEngineering is an international peer-reviewed open access quarterly 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 1600 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

  • sensors
  • environment sensors
  • remote sensing
  • wireless sensors
  • multispectral imaging
  • noninvasive imaging of crops and livestock
  • camera systems
  • unmanned aerial vehicles
  • agricultural drones
  • laser scanning thermography
  • computer vision actuators
  • controllers and autonomous robots
  • seeding
  • picking and harvesting
  • fertilizer ejectors
  • spraying
  • weed control
  • irrigation systems
  • milking robots
  • feedstuff monitoring
  • growth and productivity monitoring
  • disease detection
  • nutrition and management
  • information management
  • decision support tools

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Published Papers (6 papers)

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Research

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15 pages, 3304 KiB  
Article
Light Stress Detection in Ficus elastica with Hyperspectral Indices
by Pavel A. Dmitriev, Boris L. Kozlovsky, Anastasiya A. Dmitrieva, Tatyana V. Varduni and Vladimir S. Lysenko
AgriEngineering 2024, 6(3), 3297-3311; https://doi.org/10.3390/agriengineering6030188 - 11 Sep 2024
Viewed by 706
Abstract
The development of methods to detect plant stress is not only a scientific challenge, but is also of great importance for agriculture and forestry. However, at present, stress diagnostics based on plant spectral characteristics has several limitations: (1) the high dependence of stress [...] Read more.
The development of methods to detect plant stress is not only a scientific challenge, but is also of great importance for agriculture and forestry. However, at present, stress diagnostics based on plant spectral characteristics has several limitations: (1) the high dependence of stress assessment on plant species identity; (2) the poor differentiation of different types of stress; and (3) the difficulty of detecting stress before visible symptoms appear. In this regard, the development of plant spectral metrics represents a significant area of research. Ficus elastica plants were exposed under the photosynthetic photon flux density (PPFD) from 0 to 1200 μmol photons m−2s−1. Exposure of F. elastica leaves to excess light (EL) (≥400 μmol photons m−2s−1) resulted in an increase in reflectance in the yellow-green region (522–594 nm) and a decrease in reflectance in the red region (666–682 nm) of the spectrum, accompanied by a shift of the red edge point toward the longer wavelength. These changes were revealed using the previously proposed light stress index (LSI = mean(R666:682)/mean(R522:594)). Based on the results obtained, two new vegetation indices (VIs) were proposed: LSIRed = R674/R654 and LSINorm = (R674 − R654)/(R674 + R654), indicating light stress by changes in the red region of the spectrum. The results of the study showed that LSI, LSIRed, and LSINorm have a high degree of coupling strength with maximal quantum yields of photosystem II values. The plant response to EL exposure, as assessed by the values of these three VIs, was well expressed regardless of the PPFD levels. The effect of EL at non-stressful PPFDs (50–200 μmol photons m−2s−1) was found to disappear within one hour after cessation of exposure. In contrast, the effect of the stressful PPFD (800 μmol photons m−2s−1) was found to persist for at least 80 h after cessation of exposure. The results of the study indicate the need to consider light history in spectral monitoring of vegetation. Full article
(This article belongs to the Special Issue Sensors and Actuators for Crops and Livestock Farming)
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27 pages, 24307 KiB  
Article
Integrating Actuator Fault-Tolerant Control and Deep-Learning-Based NDVI Estimation for Precision Agriculture with a Hexacopter UAV
by Gerardo Ortiz-Torres, Manuel A. Zurita-Gil, Jesse Y. Rumbo-Morales, Felipe D. J. Sorcia-Vázquez, José J. Gascon Avalos, Alan F. Pérez-Vidal, Moises B. Ramos-Martinez, Eric Martínez Pascual and Mario A. Juárez
AgriEngineering 2024, 6(3), 2768-2794; https://doi.org/10.3390/agriengineering6030161 - 8 Aug 2024
Viewed by 1029
Abstract
This paper presents an actuator fault-tolerant control (FTC) strategy for a hexacopter unmanned aerial vehicle (UAV) designed specifically for precision agriculture applications. The proposed approach integrates advanced sensing techniques, including the estimation of Near-Infrared (NIR) reflectance from RGB imagery using the Pix2Pix deep [...] Read more.
This paper presents an actuator fault-tolerant control (FTC) strategy for a hexacopter unmanned aerial vehicle (UAV) designed specifically for precision agriculture applications. The proposed approach integrates advanced sensing techniques, including the estimation of Near-Infrared (NIR) reflectance from RGB imagery using the Pix2Pix deep learning network based on conditional Generative Adversarial Networks (cGANs), to enable the calculation of the Normalized Difference Vegetation Index (NDVI) for health assessment. Additionally, trajectory flight planning is developed to ensure the efficient coverage of the targeted agricultural area while considering the vehicle’s dynamics and fault-tolerant capabilities, even in the case of total actuator failures. The effectiveness of the proposed system is validated through simulations and real-world experiments, demonstrating its potential for reliable and accurate data collection in precision agriculture. An NDVI test was conducted on a sugarcane crop using the estimated NIR to assess the crop’s condition during its tillering stage. Therefore, the main contributions this paper include (i) the development of an actuator FTC strategy for a hexacopter UAV in precision agriculture applications, integrating advanced sensing techniques such as NIR reflectance estimation using deep learning network; (ii) the design of a flight trajectory planning method ensuring the efficient coverage of the targeted agricultural area, considering the vehicle’s dynamics and fault-tolerant capabilities; (iii) the validation of the proposed system through simulations and real-world experiments; and (iv) the successful integration of FTC scheme, advanced sensing, and flight trajectory planning for reliable and accurate data collection in precision agriculture. Full article
(This article belongs to the Special Issue Sensors and Actuators for Crops and Livestock Farming)
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13 pages, 2829 KiB  
Article
Soil Density Characterization in Management Zones Based on Apparent Soil Electrical Conductivity in Two Field Systems: Rainfeed and Center-Pivot Irrigation
by Eduardo Leonel Bottega, Cristielle König Marin, Zanandra Boff de Oliveira, Christiano de Carvalho Lamb and Telmo Jorge Carneiro Amado
AgriEngineering 2023, 5(1), 460-472; https://doi.org/10.3390/agriengineering5010030 - 23 Feb 2023
Cited by 3 | Viewed by 2189
Abstract
Understanding the spatial variability of factors that influence crop yield is essential to apply site-specific management. The present study aimed to evaluate apparent soil electrical conductivity (ECa) in two fields (A = rainfeed; B = central-pivot irrigation), based on delimited management zones (MZs). [...] Read more.
Understanding the spatial variability of factors that influence crop yield is essential to apply site-specific management. The present study aimed to evaluate apparent soil electrical conductivity (ECa) in two fields (A = rainfeed; B = central-pivot irrigation), based on delimited management zones (MZs). In each MZ, the soil density (Sd) was characterized at two soil depths, and whether the delimitation of MZs, based on the spatial variability of ECa, was able to identify regions of the field with different Sd was assessed. In general, MZs with the highest mean value of ECa also presented the highest mean values of Sd. The highest Sd values were observed in the 0.1–0.2 m layer, regardless of the studied area. Regardless of soil texture, the proposed ECa was able to detect in-field differences in Sd. The delimitation of MZs, based on the spatial variability of ECa mapping, was able to differentiate the mean values of Sd between MZ 1 (1.53 g cm−3) and MZ 2 (1.67 g cm−3) in field A, in the 0.1–0.2 m layer. A statistical difference was observed for the mean values of Sd, in MZ 1, at layer 0.1–0.2 m, when comparing the two fields: A (1.53 g cm−3) and B (1.64 g cm−3). We suggest that further studies should be carried out to confirm the efficiency of ECa in detecting the soil bulk density at different soil depths. Full article
(This article belongs to the Special Issue Sensors and Actuators for Crops and Livestock Farming)
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20 pages, 5769 KiB  
Article
Evaluation of the Water Conditions in Coffee Plantations Using RPA
by Sthéfany Airane dos Santos, Gabriel Araújo e Silva Ferraz, Vanessa Castro Figueiredo, Margarete Marin Lordelo Volpato, Marley Lamounier Machado and Vânia Aparecida Silva
AgriEngineering 2023, 5(1), 65-84; https://doi.org/10.3390/agriengineering5010005 - 29 Dec 2022
Cited by 4 | Viewed by 2093
Abstract
The objective of this study is to evaluate the water conditions in a coffee plantation using precision agriculture (PA) techniques associated with geostatistics and high-resolution images. The study area is 1.2 ha of coffee crops of the Topázio MG 1190 cultivar. Two data [...] Read more.
The objective of this study is to evaluate the water conditions in a coffee plantation using precision agriculture (PA) techniques associated with geostatistics and high-resolution images. The study area is 1.2 ha of coffee crops of the Topázio MG 1190 cultivar. Two data collections were performed: one in the dry season and one in the rainy season. A total of 30 plants were marked and georeferenced within the study area. High-resolution images were obtained using a remotely piloted aircraft (RPA) equipped with a multispectral sensor. Leaf water potential was obtained using a Scholander pump. The spatialization and interpolation of the leaf water potential data were performed by geostatistical analysis. The vegetation indices were calculated through the images obtained by the RPA and were used for a regression and correlation analysis, together with the water potential data. The degree of spatial dependence (DSD) obtained by the geostatistical data showed strong spatial dependence for both periods evaluated. In the correlation analysis and linear regression, only the red band showed a significant correlation (39.93%) with an R² of 15.95%. The geostatistical analysis was an important tool for the spatialization of the water potential variable; conversely, the use of vegetation indexes obtained by the RPA was not as efficient in the evaluation of the water conditions of the coffee plants. Full article
(This article belongs to the Special Issue Sensors and Actuators for Crops and Livestock Farming)
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Review

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24 pages, 2036 KiB  
Review
Optical Methods for the Detection of Plant Pathogens and Diseases (Review)
by Sergey V. Gudkov, Tatiana A. Matveeva, Ruslan M. Sarimov, Alexander V. Simakin, Evgenia V. Stepanova, Maksim N. Moskovskiy, Alexey S. Dorokhov and Andrey Yu. Izmailov
AgriEngineering 2023, 5(4), 1789-1812; https://doi.org/10.3390/agriengineering5040110 - 9 Oct 2023
Cited by 7 | Viewed by 3275
Abstract
Plant diseases of an infectious nature are the reason for major economic losses in agriculture throughout the world. The early, rapid and non-invasive detection of diseases and pathogens is critical for effective control. Optical diagnostic methods have a high speed of analysis and [...] Read more.
Plant diseases of an infectious nature are the reason for major economic losses in agriculture throughout the world. The early, rapid and non-invasive detection of diseases and pathogens is critical for effective control. Optical diagnostic methods have a high speed of analysis and non-invasiveness. The review provides a general description of such methods and also discusses in more detail methods based on the scattering and absorption of light in the UV, Vis, IR and terahertz ranges, Raman scattering and LiDAR technologies. The application of optical methods to all parts of plants, to a large number of groups of pathogens, under various data collection conditions is considered. The review reveals the diversity and achievements of modern optical methods in detecting infectious plant diseases, their development trends and their future potential. Full article
(This article belongs to the Special Issue Sensors and Actuators for Crops and Livestock Farming)
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Other

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12 pages, 3259 KiB  
Technical Note
Evaluation of Ultrasonic Sensor for Precision Liquid Volume Measurement in Narrow Tubes and Pipes
by Benjamin C. Smith, Ryan W. Bergman and Matthew J. Darr
AgriEngineering 2023, 5(1), 287-298; https://doi.org/10.3390/agriengineering5010019 - 1 Feb 2023
Viewed by 3047
Abstract
The introduction of computer vision and machine learning into agricultural systems has produced significant new opportunities for high precision application of liquid products in both grain and livestock agriculture. These technologies, which enable liquid application in site-specific, non-broadcast applications, are driving new evaluations [...] Read more.
The introduction of computer vision and machine learning into agricultural systems has produced significant new opportunities for high precision application of liquid products in both grain and livestock agriculture. These technologies, which enable liquid application in site-specific, non-broadcast applications, are driving new evaluations of nozzle technologies which apply a consistent dose of liquid product in a non-conventional manner compared to historic perceptions. This field of innovation is driving the need for improved high-capacity systems for evaluating nozzle performance in high-precision applications. Historically, patternator tables with volumetric measurements of total applied liquid have served as the standard for fluid nozzle evaluation. These volumetric measurements are based on measuring the displaced distance of liquid over a defined time to determine flow rate. However, current distance sensors present challenges for achieving small-volume measurements and enabling automation at a scale necessary to meet innovation demands of high-precision nozzle systems. A novel concept for high speed and automated measurement of a high precision patternator table was developed using an ultrasonic sensor and a carefully designed liquid retainment system to maximize measurement precision. The performance of this system was quantified by comparing calibrations and performance across different vessels for volume measurement (tubes and pipes) used in the application of a nozzle patternator. A total of three square tubes (15.9, 22.3, 31.0 mm widths) and three pipes (25.2, 27.0, 35.1 mm diameters) were evaluated, with the 27 mm pipe matching the ultrasonic sensor’s rating. All calibrations were successful, depicting linear characteristics with R2 > 0.99. The smallest pipe presented issues for the sensor to measure in post-calibration and was thus not evaluated further. The residual values from operational performance highlight that the 25.2 mm tube and the 27.0 mm pipe are highly accurate with no indication of bias or non-normality. The relative uncertainty ranges from 2.9 to 42% (350 mL to 25 mL) depending on the tube and pipe cross-sectional diameter or width with the sensor accuracy and uncertainty in the tube and pipe area being the largest factors. The results of this study indicate that the 25.2 mm tube and the 27.0 mm pipe could be excellent options for autonomous liquid volume measurement with the ultrasonic sensor. A key challenge identified in this study is that the assumptions in the sensor’s intrinsic calibration are violated with the tubes and pipes evaluated. Full article
(This article belongs to the Special Issue Sensors and Actuators for Crops and Livestock Farming)
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