Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
3.3
Journals
Agriculture
Special Issues
Smart Agriculture Sensors and Monitoring Systems for Field Detection
share announcement

Smart Agriculture Sensors and Monitoring Systems for Field Detection

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 25 March 2025 | Viewed by 5339

Special Issue Editor

Dr. Adrian Gracia-Romero

E-Mail Website1 Website2
Guest Editor
Field Crops Program, Institute for Food and Agricultural Research and Technology (IRTA), 251981 Lleida, Spain
Interests: plant phenotyping; remote sensing; UAV; cereal; wheat; drought; RGB; thermal imaging; multiespectral sensors

Special Issue Information

Dear Colleagues,

Contemporary precision agriculture and breeding programs involve the evaluation of numerous plots across extensive experimental fields and are limited by the traditional methodologies of crop monitoring that are laborious and time-consuming, sometimes require destructive samplings, or rely on subjective visual samplings. Innovation in terms of smart agriculture systems has emerged as a potential solution to improve the throughput capacity and precision of these standard practices of crop evaluation in large-scale experimental studies. The idea of smart agriculture implies the use of new technologies in order to increase the yields and quality of crops, optimizing resources and reducing environmental impacts. Currently, the implementation of these methodologies encompasses a range of sensors and monitoring devices designed to improve the evaluation of a diversity of aspects as soil health and environmental conditions to the use of proximal remote sensing technology to evaluate plant physiologic and agronomic parameters.

This Special Issue aims to highlight impactful research on how the increasing appearance of new devices could contribute towards better crop management, including from real-time meteorological data, soil health parameters, or in vivo sensors based on the stem or leaves, monitoring physiological changes to proximal remote sensing tools at the leaf, canopy, and UAV scales. This Special Issue welcomes the submission of studies based on precision agriculture and plant phenotyping and which cover a broad range of crops. Moreover, we also encourage studies which focus on exploring synergies between sensors in order to define traits of importance to made agronomic decisions. All types of articles, such as original research, opinions, and reviews, are welcome to be submitted to this Special Issue.

Dr. Adrian Gracia-Romero
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. Agriculture 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

  • precision agriculture
  • plant phenotyping
  • smart agriculture
  • sensors
  • proximal remote sensing
  • UAV

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 33216 KiB  
Article
System Design for a Prototype Acoustic Network to Deter Avian Pests in Agriculture Fields
by Destiny Kwabla Amenyedzi, Micheline Kazeneza, Ipyana Issah Mwaisekwa, Frederic Nzanywayingoma, Philibert Nsengiyumva, Peace Bamurigire, Emmanuel Ndashimye and Anthony Vodacek
Agriculture 2025, 15(1), 10; https://doi.org/10.3390/agriculture15010010 - 24 Dec 2024
Viewed by 877
Abstract
Crop damage attributed to pest birds is an important problem, particularly in low-income countries. This paper describes a prototype system for pest bird detection using a Conv1D neural network model followed by scaring actions to reduce the presence of pest birds on farms. [...] Read more.
Crop damage attributed to pest birds is an important problem, particularly in low-income countries. This paper describes a prototype system for pest bird detection using a Conv1D neural network model followed by scaring actions to reduce the presence of pest birds on farms. Acoustic recorders were deployed on farms for data collection, supplemented by acoustic libraries. The sounds of pest bird species were identified and labeled. The labeled data were used in Edge Impulse to train a tinyML Conv1D model to detect birds of interest. The model was deployed on Arduino Nano 33 BLE Sense (nodes) and XIAO (Base station) microcontrollers to detect the pest birds, and based on the detection, scaring sounds were played to deter the birds. The model achieved an accuracy of 96.1% during training and 92.99% during testing. The testing F1 score was 0.94, and the ROC score was 0.99, signifying a good discriminatory ability of the model. The prototype was able to make inferences in 53 ms using only 14.8 k of peak RAM and only 43.8 K of flash memory to store the model. Results from the prototype deployment in the field demonstrated successful detection and triggering actions and SMS messaging notifications. Further development of this novel integrated and sustainable solution will add another tool for dealing with pest birds. Full article
(This article belongs to the Special Issue Smart Agriculture Sensors and Monitoring Systems for Field Detection)
►▼ Show Figures

Figure 1

19 pages, 18432 KiB  
Article
Low-Cost Lettuce Height Measurement Based on Depth Vision and Lightweight Instance Segmentation Model
by Yiqiu Zhao, Xiaodong Zhang, Jingjing Sun, Tingting Yu, Zongyao Cai, Zhi Zhang and Hanping Mao
Agriculture 2024, 14(9), 1596; https://doi.org/10.3390/agriculture14091596 - 13 Sep 2024
Cited by 1 | Viewed by 1123
Abstract
Plant height is a crucial indicator of crop growth. Rapid measurement of crop height facilitates the implementation and management of planting strategies, ensuring optimal crop production quality and yield. This paper presents a low-cost method for the rapid measurement of multiple lettuce heights, [...] Read more.
Plant height is a crucial indicator of crop growth. Rapid measurement of crop height facilitates the implementation and management of planting strategies, ensuring optimal crop production quality and yield. This paper presents a low-cost method for the rapid measurement of multiple lettuce heights, developed using an improved YOLOv8n-seg model and the stacking characteristics of planes in depth images. First, we designed a lightweight instance segmentation model based on YOLOv8n-seg by enhancing the model architecture and reconstructing the channel dimension distribution. This model was trained on a small-sample dataset augmented through random transformations. Secondly, we proposed a method to detect and segment the horizontal plane. This method leverages the stacking characteristics of the plane, as identified in the depth image histogram from an overhead perspective, allowing for the identification of planes parallel to the camera’s imaging plane. Subsequently, we evaluated the distance between each plane and the centers of the lettuce contours to select the cultivation substrate plane as the reference for lettuce bottom height. Finally, the height of multiple lettuce plants was determined by calculating the height difference between the top and bottom of each plant. The experimental results demonstrated that the improved model achieved a 25.56% increase in processing speed, along with a 2.4% enhancement in mean average precision compared to the original YOLOv8n-seg model. The average accuracy of the plant height measurement algorithm reached 94.339% in hydroponics and 91.22% in pot cultivation scenarios, with absolute errors of 7.39 mm and 9.23 mm, similar to the sensor’s depth direction error. With images downsampled by a factor of 1/8, the highest processing speed recorded was 6.99 frames per second (fps), enabling the system to process an average of 174 lettuce targets per second. The experimental results confirmed that the proposed method exhibits promising accuracy, efficiency, and robustness. Full article
(This article belongs to the Special Issue Smart Agriculture Sensors and Monitoring Systems for Field Detection)
►▼ Show Figures

Figure 1

17 pages, 4179 KiB  
Article
Strategy for Monitoring the Blast Incidence in Crops of Bomba Rice Variety Using Remote Sensing Data
by Alba Agenjos-Moreno, Constanza Rubio, Antonio Uris, Rubén Simeón, Belén Franch, Concha Domingo and Alberto San Bautista
Agriculture 2024, 14(8), 1385; https://doi.org/10.3390/agriculture14081385 - 16 Aug 2024
Viewed by 832
Abstract
In this paper, we investigated the monitoring and characterization of the pest Magnaporthe oryzae, known as rice blast, in the Bomba rice variety at the Albufera Natural Park, located in Valencia, Spain during the 2022 and 2023 seasons. Using reflectance data from [...] Read more.
In this paper, we investigated the monitoring and characterization of the pest Magnaporthe oryzae, known as rice blast, in the Bomba rice variety at the Albufera Natural Park, located in Valencia, Spain during the 2022 and 2023 seasons. Using reflectance data from different Sentinel-2 satellite bands, various vegetative indices were calculated for each year. Significant differences in reflectance in the visible (B4), infrared (B8), red-edge (B6 and B7), and SWIR (B11) bands were detected between healthy and unhealthy fields. Additionally, variations were observed in the vegetation indices, with RVI and IRECI standing out for their higher accuracy in identifying blast-affected plots compared to NDVI and NDRE. Early differences in band values, vegetative indices, and spectral signatures were observed between the unhealthy and healthy plots, allowing for the anticipation of control treatments, whose effectiveness relies on timely intervention. Full article
(This article belongs to the Special Issue Smart Agriculture Sensors and Monitoring Systems for Field Detection)
►▼ Show Figures

Figure 1

15 pages, 4404 KiB  
Article
Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton
by Sahila Beegum, Muhammad Adeel Hassan, Purushothaman Ramamoorthy, Raju Bheemanahalli, Krishna N. Reddy, Vangimalla Reddy and Kambham Raja Reddy
Agriculture 2024, 14(7), 1054; https://doi.org/10.3390/agriculture14071054 - 29 Jun 2024
Viewed by 1705
Abstract
Cotton is a pivotal global commodity underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars for variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial to breeding programs, is often viewed as [...] Read more.
Cotton is a pivotal global commodity underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars for variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial to breeding programs, is often viewed as a bottleneck due to the inefficiency of traditional, low-throughput methods. To address this limitation, this study utilizes hyperspectral remote sensing, a promising tool for assessing crucial crop traits across forty cotton varieties. The results from this study demonstrated the effectiveness of four vegetation indices (VIs) in evaluating these varieties for water-use efficiency (WUE). The prediction accuracy for WUE through VIs such as the simple ratio water index (SRWI) and normalized difference water index (NDWI) was higher (up to R2 = 0.66), enabling better detection of phenotypic variations (p < 0.05) among the varieties compared to physiological-related traits (from R2 = 0.21 to R2 = 0.42), with high repeatability and a low RMSE. These VIs also showed high Pearson correlations with WUE (up to r = 0.81) and yield-related traits (up to r = 0.63). We also selected high-performing varieties based on the VIs, WUE, and fiber quality traits. This study demonstrated that the hyperspectral-based proximal sensing approach helps rapidly assess the in-season performance of varieties for imperative traits and aids in precise breeding decisions. Full article
(This article belongs to the Special Issue Smart Agriculture Sensors and Monitoring Systems for Field Detection)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop