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Innovative Imaging Sensors Combined with Artificial Intelligence Approaches to Support Precision Agriculture

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 5801

Special Issue Editors

Dr. Simona Violino

E-Mail Website
Guest Editor
Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA)—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare 16, Monterotondo, 00015 Rome, Italy
Interests: Vis-NIR spectroscopy (hyperspectral imaging and punctual analysis); food traceability; infotracing and blockchain systems; electronic traceability; multivariate statistics
Dr. Loredana Canfora

E-Mail Website
Guest Editor
Research Centre for Agriculture and Environment, Rome, Italy
Interests: soil biodiversity; microbial ecology; integrated pest management; evaluation of impact on soil microbial community; assessment of new tool for detection of bio-inoculant in soil
Dr. Francesca Antonucci

E-Mail Website
Guest Editor
Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA)—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare 16, Monterotondo, 00015 Rome, Italy
Interests: machine vision retrofit system for mechanical weed control in precision agriculture applications
Dr. Roberto Ciccoritti

E-Mail Website1 Website2
Guest Editor
CREA—Research Centre for Olive, Fruit and Citrus Crops, Via di Fioranello 52, 00134 Rome, Italy
Interests: food processing; biochemistry; chromatography; spectrometry; bioactive compounds; extraction; antioxidant activity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, technology has played an important role in agronomic management. The use of advanced tools that incorporate a wide range of sensors to detect and monitor crop status (e.g., vegetative index, phytopathology, water stress, fertility) allows rapid, non-invasive and low-cost real-time analyses.

Generally, image processing and computer vision applications allow us to reduce equipment costs and to increase interest in non-destructive agriculture assessment methods. Precision agriculture represents an integrated, information- and production-based farming system improving long-term, site-specific and whole farm production efficiency and profitability. This could limit the undesirable effects of an excess/lack of chemical loading to the environment or productivity loss due to unsuitable input application. The advantage is that precision agriculture will provide a wide range of economic and environmental benefits with a high and accurate precision level.

In this context, this Special Issue will concern topics about the use of applied sensors (e.g., opto-electronics, spectrophotometry, thermography, RGB cameras, drones) in combination with artificial intelligence (e.g., predictive modeling, neural networks, IoT, etc.) for proximal and remote sensing analysis.

Dr. Simona Violino
Dr. Loredana Canfora
Dr. Francesca Antonucci
Dr. Roberto Ciccoritti
Guest Editors

Manuscript Submission Information

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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

  • image analysis
  • multivariate statistics
  • opto-electronic applications
  • sustainable agriculture
  • neural networks
  • proximal sensing
  • remote sensing
  • UAV

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Published Papers (4 papers)

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Research

15 pages, 3973 KiB  
Article
Probing Biological Nitrogen Fixation in Legumes Using Raman Spectroscopy
by Abdolabbas Jafari, Kritarth Seth, Armin Werner, Shengjing Shi, Rainer Hofmann and Valerio Hoyos-Villegas
Sensors 2024, 24(15), 4944; https://doi.org/10.3390/s24154944 - 30 Jul 2024
Viewed by 819
Abstract
Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman [...] Read more.
Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman spectra were obtained from soybean plants grown with and without rhizobia bacteria to identify spectral signatures associated with BNF. δN15 isotope ratio mass spectrometry (IRMS) was used to determine actual BNF percentages. Partial least squares regression (PLSR) was employed to develop a model for BNF quantification based on Raman spectra. The model explained 80% of the variation in BNF activity. To enhance the model’s specificity for BNF detection regardless of nitrogen availability, a subsequent elastic net (Enet) regularisation strategy was implemented. This approach provided insights into key wavenumbers and biochemicals associated with BNF in soybeans. Full article
(This article belongs to the Special Issue Innovative Imaging Sensors Combined with Artificial Intelligence Approaches to Support Precision Agriculture)
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18 pages, 3945 KiB  
Article
3D Camera and Single-Point Laser Sensor Integration for Apple Localization in Spindle-Type Orchard Systems
by R. M. Rasika D. Abeyrathna, Victor Massaki Nakaguchi, Zifu Liu, Rizky Mulya Sampurno and Tofael Ahamed
Sensors 2024, 24(12), 3753; https://doi.org/10.3390/s24123753 - 9 Jun 2024
Viewed by 1165
Abstract
Accurate localization of apples is the key factor that determines a successful harvesting cycle in the automation of apple harvesting for unmanned operations. In this regard, accurate depth sensing or positional information of apples is required for harvesting apples based on robotic systems, [...] Read more.
Accurate localization of apples is the key factor that determines a successful harvesting cycle in the automation of apple harvesting for unmanned operations. In this regard, accurate depth sensing or positional information of apples is required for harvesting apples based on robotic systems, which is challenging in outdoor environments because of uneven light variations when using 3D cameras for the localization of apples. Therefore, this research attempted to overcome the effect of light variations for the 3D cameras during outdoor apple harvesting operations. Thus, integrated single-point laser sensors for the localization of apples using a state-of-the-art model, the EfficientDet object detection algorithm with an [email protected] of 0.775 were used in this study. In the experiments, a RealSense D455f RGB-D camera was integrated with a single-point laser ranging sensor utilized to obtain precise apple localization coordinates for implementation in a harvesting robot. The single-point laser range sensor was attached to two servo motors capable of moving the center position of the detected apples based on the detection ID generated by the DeepSORT (online real-time tracking) algorithm. The experiments were conducted under indoor and outdoor conditions in a spindle-type apple orchard artificial architecture by mounting the combined sensor system behind a four-wheel tractor. The localization coordinates were compared between the RGB-D camera depth values and the combined sensor system under different light conditions. The results show that the root-mean-square error (RMSE) values of the RGB-D camera depth and integrated sensor mechanism varied from 3.91 to 8.36 cm and from 1.62 to 2.13 cm under 476~600 lx to 1023~1100 × 100 lx light conditions, respectively. The integrated sensor system can be used for an apple harvesting robotic manipulator with a positional accuracy of ±2 cm, except for some apples that were occluded due to leaves and branches. Further research will be carried out using changes in the position of the integrated system for recognition of the affected apples for harvesting operations. Full article
(This article belongs to the Special Issue Innovative Imaging Sensors Combined with Artificial Intelligence Approaches to Support Precision Agriculture)
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18 pages, 3609 KiB  
Article
A Performance Evaluation of Two Hyperspectral Imaging Systems for the Prediction of Strawberries’ Pomological Traits
by Tiziana Amoriello, Roberto Ciorba, Gaia Ruggiero, Monica Amoriello and Roberto Ciccoritti
Sensors 2024, 24(1), 174; https://doi.org/10.3390/s24010174 - 28 Dec 2023
Cited by 1 | Viewed by 1493
Abstract
Pomological traits are the major factors determining the quality and price of fresh fruits. This research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems in the wavelength regions comprising visible to near infrared (VisNIR) (400−1000 nm) and short-wave [...] Read more.
Pomological traits are the major factors determining the quality and price of fresh fruits. This research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems in the wavelength regions comprising visible to near infrared (VisNIR) (400−1000 nm) and short-wave infrared (SWIR) (935−1720 nm) for predicting four strawberry quality attributes (firmness—FF, total soluble solid content—TSS, titratable acidity—TA, and dry matter—DM). Prediction models were developed based on artificial neural networks (ANN). The entire strawberry VisNIR reflectance spectra resulted in accurate predictions of TSS (R2 = 0.959), DM (R2 = 0.947), and TA (R2 = 0.877), whereas good prediction was observed for FF (R2 = 0.808). As for models from the SWIR system, good correlations were found between each of the physicochemical indices and the spectral information (R2 = 0.924 for DM; R2 = 0.898 for TSS; R2 = 0.953 for TA; R2 = 0.820 for FF). Finally, data fusion demonstrated a higher ability to predict fruit internal quality (R2 = 0.942 for DM; R2 = 0. 981 for TSS; R2 = 0.976 for TA; R2 = 0.951 for FF). The results confirmed the potential of these two HSI systems as a rapid and nondestructive tool for evaluating fruit quality and enhancing the product’s marketability. Full article
(This article belongs to the Special Issue Innovative Imaging Sensors Combined with Artificial Intelligence Approaches to Support Precision Agriculture)
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17 pages, 5322 KiB  
Article
Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks
by Krzysztof Strzępek, Mateusz Salach, Bartosz Trybus, Karol Siwiec, Bartosz Pawłowicz and Andrzej Paszkiewicz
Sensors 2023, 23(22), 9251; https://doi.org/10.3390/s23229251 - 17 Nov 2023
Cited by 1 | Viewed by 1433
Abstract
This article presents an integrated system that uses the capabilities of unmanned aerial vehicles (UAVs) to perform a comprehensive crop analysis, combining qualitative and quantitative evaluations for efficient agricultural management. A convolutional neural network-based model, Detectron2, serves as the foundation for detecting and [...] Read more.
This article presents an integrated system that uses the capabilities of unmanned aerial vehicles (UAVs) to perform a comprehensive crop analysis, combining qualitative and quantitative evaluations for efficient agricultural management. A convolutional neural network-based model, Detectron2, serves as the foundation for detecting and segmenting objects of interest in acquired aerial images. This model was trained on a dataset prepared using the COCO format, which features a variety of annotated objects. The system architecture comprises a frontend and a backend component. The frontend facilitates user interaction and annotation of objects on multispectral images. The backend involves image loading, project management, polygon handling, and multispectral image processing. For qualitative analysis, users can delineate regions of interest using polygons, which are then subjected to analysis using the Normalized Difference Vegetation Index (NDVI) or Optimized Soil Adjusted Vegetation Index (OSAVI). For quantitative analysis, the system deploys a pre-trained model capable of object detection, allowing for the counting and localization of specific objects, with a focus on young lettuce crops. The prediction quality of the model has been calculated using the AP (Average Precision) metric. The trained neural network exhibited robust performance in detecting objects, even within small images. Full article
(This article belongs to the Special Issue Innovative Imaging Sensors Combined with Artificial Intelligence Approaches to Support Precision Agriculture)
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