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Artificial Intelligence and Sensor Technologies in Agri-Food

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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 (28 March 2024) | Viewed by 10502

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Special Issue Editors

Dr. Georgios Leontidis

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Guest Editor

Interdisciplinary Centre for Data and AI, School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3FX, UK
Interests: deep learning; multimodal learning; capsule neural networks; self-supervised learning; domain adaptation; privacy-preserving technologies; efficient deep learning systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Simon Pearson

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Guest Editor

Lincoln Institute for Agri-Food Technology, University of Lincoln, Lincoln LN6 7TS, UK
Interests: agricultural robotics and automation; environmental physiology of fresh produce and ornamental crops; modified atmosphere packaging; farm decision support systems
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Prof. Dr. Marc Hanheide

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Guest Editor

School of Computer Science, University of Lincoln, Lincoln LN67TS, UK
Interests: AI; autonomous robots; human-robot interaction; interaction-enabling technologies; agri-robotics

Dr. Jinya Su

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Guest Editor

School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3UE, UK
Interests: intelligent autonomous systems; satellite/UAV remote sensing; precision agriculture; artificial intelligence; control engineering
Special Issues, Collections and Topics in MDPI journals

Dr. Spyros Fountas

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Guest Editor

Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece
Interests: precision agriculture; UAV; information systems; farm machinery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The agri-food system is undergoing a rapid digital transformation that connects local and global supply chains to address economic, environmental, and societal challenges. It is estimated that 70% more food will be needed by 2050 to feed the ever-growing global population sustainably, projected to rise to about 9.2 billion, whilst aiming at meeting the sector’s ambitious net zero targets (Food and Agriculture Organization of the United Nations). Considering that a total of 842 million people is estimated to be suffering from chronic hunger - regularly not getting enough food to conduct an active life - novel solutions are needed to accelerate food production and contribute to the sector’s decarbonization. Artificial Intelligence (AI), Robotics, and new sensor technologies can be transformative towards supporting the sector’s journey to increase productivity across the agri-food supply chain, contribute to environmental and financial sustainability, and achieve the sustainable development goals.

This special issue aims at disseminating the latest original research and reviews in exploiting new theoretical advancements, applications, hardware and software systems, and new challenges in the field of agri-food.

Potential topics, applied to any area within agri-food, include but are not limited to:

Novel sensors and wireless sensor networks for internet of agri-food things
Multimodal learning and domain adaptation for agri-food data
Agri-food data sharing and privacy, such as federated learning, fully homomorphic encryption, differential privacy, etc.
End-to-end deep learning systems for precision agriculture
Agri-robotic systems in the wild
Human-Robot Collaboration in agriculture
AI-enabled smart polytunnel systems
Multi-task learning for decision making under uncertainty
Remote sensing and UAVs in agriculture
Efficient and sparse machine learning systems
Integration of robotic and computer vision systems
Neuro-symbolic systems for explainable and transparent decision making in agri-food
Natural language generation systems for automated reporting to agri-food stakeholders
Advanced yield forecasting and sustainability action planning systems

Dr. Georgios Leontidis
Prof. Dr. Simon Pearson
Prof. Dr. Marc Hanheide
Dr. Jinya Su
Dr. Spyros Fountas
Guest Editors

Manuscript Submission Information

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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

novel sensors
sensor networks
digital agriculture
machine learning
agri-robotics
efficient AI systems
remote sensing and UAVs
internet of agri-food things
privacy-preserving technologies

Published Papers (7 papers)

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Research

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16 pages, 6881 KiB

Open AccessArticle

DFSNet: A 3D Point Cloud Segmentation Network toward Trees Detection in an Orchard Scene

by Xinrong Bu, Chao Liu, Hui Liu, Guanxue Yang, Yue Shen and Jie Xu

Sensors 2024, 24(7), 2244; https://doi.org/10.3390/s24072244 - 31 Mar 2024

Viewed by 393

Abstract

In order to guide orchard management robots to realize some tasks in orchard production such as autonomic navigation and precision spraying, this research proposed a deep-learning network called dynamic fusion segmentation network (DFSNet). The network contains a local feature aggregation (LFA) layer and a dynamic fusion segmentation architecture. The LFA layer uses the positional encoders for initial transforming embedding, and progressively aggregates local patterns via the multi-stage hierarchy. The fusion segmentation module (Fus-Seg) can format point tags by learning a multi-embedding space, and the generated tags can further mine the point cloud features. At the experimental stage, significant segmentation results of the DFSNet were demonstrated on the dataset of orchard fields, achieving an accuracy rate of 89.43% and an mIoU rate of 74.05%. DFSNet outperforms other semantic segmentation networks, such as PointNet, PointNet++, D-PointNet++, DGCNN, and Point-NN, with improved accuracies over them by 11.73%, 3.76%, 2.36%, and 2.74%, respectively, and improved mIoUs over the these networks by 28.19%, 9.89%, 6.33%, 9.89, and 24.69%, respectively, on the all-scale dataset (simple-scale dataset + complex-scale dataset). The proposed DFSNet can capture more information from orchard scene point clouds and provide more accurate point cloud segmentation results, which are beneficial to the management of orchards. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensor Technologies in Agri-Food)

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17 pages, 5130 KiB

Open AccessArticle

Performance Improvement of Partial Least Squares Regression Soluble Solid Content Prediction Model Based on Adjusting Distance between Light Source and Spectral Sensor according to Apple Size

by Doo-Jin Song, Seung-Woo Chun, Min-Jee Kim, Soo-Hwan Park, Chi-Kook Ahn and Changyeun Mo

Sensors 2024, 24(2), 316; https://doi.org/10.3390/s24020316 - 05 Jan 2024

Viewed by 583

Abstract

Apples are widely cultivated in the Republic of Korea and are preferred by consumers for their sweetness. Soluble solid content (SSC) is measured non-destructively using near-infrared (NIR) spectroscopy; however, the SSC measurement error increases with the change in apple size since the distance between the light source and the near-infrared sensor is fixed. In this study, spectral characteristics caused by the differences in apple size were investigated. An optimal SSC prediction model applying partial least squares regression (PLSR) to three measurement conditions based on apple size was developed. The three optimal measurement conditions under which the Vis/NIR spectrum is less affected by six apple size levels (Levels I–VI) were selected. The distance from the apple center to the light source and that to the sensor were 125 and 75 mm (Distance 1), 123 and 75 mm (Distance 2), and 135 and 80 mm (Distance 3). The PLSR model applying multiplicative scatter correction pretreatment under Distance 3 measurement conditions showed the best performance for Level IV-sized apples (

R_{p r e}^{2}

= 0.91, RMSEP = 0.508 °Brix). This study shows the possibility of improving the SSC prediction performance of apples by adjusting the distance between the light source and the NIR sensor according to fruit size. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensor Technologies in Agri-Food)

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16 pages, 2809 KiB

Open AccessArticle

AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden

by Oliver J. Fisher, Ahmed Rady, Aly A. A. El-Banna, Haitham H. Emaish and Nicholas J. Watson

Sensors 2023, 23(21), 8671; https://doi.org/10.3390/s23218671 - 24 Oct 2023

Cited by 1 | Viewed by 1029

Abstract

The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their deployment is hindered by the high cost of labelling the crop images to provide data for model training. This study examines the capabilities of semi-supervised and active learning to minimise effort when labelling cotton lint samples while maintaining high classification accuracy. Random forest classification models were developed using supervised learning, semi-supervised learning, and active learning to determine Egyptian cotton grade. Compared to supervised learning (80.20–82.66%) and semi-supervised learning (81.39–85.26%), active learning models were able to achieve higher accuracy (82.85–85.33%) with up to 46.4% reduction in the volume of labelled data required. The primary obstacle when using machine learning for Egyptian cotton grading is the time required for labelling cotton lint samples. However, by applying active learning, this study successfully decreased the time needed from 422.5 to 177.5 min. The findings of this study demonstrate that active learning is a promising approach for developing accurate and efficient machine learning models for grading food and industrial crops. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensor Technologies in Agri-Food)

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20 pages, 3405 KiB

Open AccessArticle

Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from Smartphones

by Shufeng Zhang, Yuxi Chen, Weizhen Liu, Bang Liu and Xiang Zhou

Sensors 2023, 23(11), 5135; https://doi.org/10.3390/s23115135 - 28 May 2023

Cited by 2 | Viewed by 1713

Abstract

Marbling characteristics are important traits for the genetic improvement of pork quality. Accurate marbling segmentation is the prerequisite for the quantification of these traits. However, the marbling targets are small and thin with dissimilar sizes and shapes and scattered in pork, complicating the segmentation task. Here, we proposed a deep learning-based pipeline, a shallow context encoder network (Marbling-Net) with the usage of patch-based training strategy and image up-sampling to accurately segment marbling regions from images of pork longissimus dorsi (LD) collected by smartphones. A total of 173 images of pork LD were acquired from different pigs and released as a pixel-wise annotation marbling dataset, the pork marbling dataset 2023 (PMD2023). The proposed pipeline achieved an IoU of 76.8%, a precision of 87.8%, a recall of 86.0%, and an F1-score of 86.9% on PMD2023, outperforming the state-of-art counterparts. The marbling ratios in 100 images of pork LD are highly correlated with marbling scores and intramuscular fat content measured by the spectrometer method (

R^{2}

= 0.884 and 0.733, respectively), demonstrating the reliability of our method. The trained model could be deployed in mobile platforms to accurately quantify pork marbling characteristics, benefiting the pork quality breeding and meat industry. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensor Technologies in Agri-Food)

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24 pages, 1842 KiB

Open AccessArticle

EDLaaS:Fully Homomorphic Encryption over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting

by George Onoufriou, Marc Hanheide and Georgios Leontidis

Sensors 2022, 22(21), 8124; https://doi.org/10.3390/s22218124 - 24 Oct 2022

Cited by 1 | Viewed by 1570

Abstract

We present automatically parameterised Fully Homomorphic Encryption (FHE) for encrypted neural network inference and exemplify our inference over FHE-compatible neural networks with our own open-source framework and reproducible examples. We use the fourth generation Cheon, Kim, Kim, and Song (CKKS) FHE scheme over fixed points provided by the Microsoft Simple Encrypted Arithmetic Library (MS-SEAL). We significantly enhance the usability and applicability of FHE in deep learning contexts, with a focus on the constituent graphs, traversal, and optimisation. We find that FHE is not a panacea for all privacy-preserving machine learning (PPML) problems and that certain limitations still remain, such as model training. However, we also find that in certain contexts FHE is well-suited for computing completely private predictions with neural networks. The ability to privately compute sensitive problems more easily while lowering the barriers to entry can allow otherwise too-sensitive fields to begin advantaging themselves of performant third-party neural networks. Lastly, we show how encrypted deep learning can be applied to a sensitive real-world problem in agri-food, i.e., strawberry yield forecasting, demonstrating competitive performance. We argue that the adoption of encrypted deep learning methods at scale could allow for a greater adoption of deep learning methodologies where privacy concerns exist, hence having a large positive potential impact within the agri-food sector and its journey to net zero. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensor Technologies in Agri-Food)

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12 pages, 1139 KiB

Open AccessArticle

Domain Adaptation for In-Line Allergen Classification of Agri-Food Powders Using Near-Infrared Spectroscopy

by Alexander Lewis Bowler, Samet Ozturk, Ahmed Rady and Nicholas Watson

Sensors 2022, 22(19), 7239; https://doi.org/10.3390/s22197239 - 24 Sep 2022

Cited by 5 | Viewed by 1563

Abstract

The addition of incorrect agri-food powders to a production line due to human error is a large safety concern in food and drink manufacturing, owing to incorporation of allergens in the final product. This work combines near-infrared spectroscopy with machine-learning models for early detection of this problem. Specifically, domain adaptation is used to transfer models from spectra acquired under stationary conditions to moving samples, thereby minimizing the volume of labelled data required to collect on a production line. Two deep-learning domain-adaptation methodologies are used: domain-adversarial neural networks and semisupervised generative adversarial neural networks. Overall, accuracy of up to 96.0% was achieved using no labelled data from the target domain moving spectra, and up to 99.68% was achieved when incorporating a single labelled data instance for each material into model training. Using both domain-adaptation methodologies together achieved the highest prediction accuracies on average, as did combining measurements from two near-infrared spectroscopy sensors with different wavelength ranges. Ensemble methods were used to further increase model accuracy and provide quantification of model uncertainty, and a feature-permutation method was used for global interpretability of the models. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensor Technologies in Agri-Food)

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Review

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38 pages, 10494 KiB

Open AccessReview

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

by Willow Mandil, Vishnu Rajendran, Kiyanoush Nazari and Amir Ghalamzan-Esfahani

Sensors 2023, 23(17), 7362; https://doi.org/10.3390/s23177362 - 23 Aug 2023

Cited by 3 | Viewed by 2303

Abstract

Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and extracting vital physical features. This comprehensive review paper presents an in-depth overview of the growing research on tactile-sensing technologies, encompassing state-of-the-art techniques, future prospects, and current limitations. The paper focuses on tactile hardware, algorithmic complexities, and the distinct features offered by each sensor. This paper has a special emphasis on agri-food manipulation and relevant tactile-sensing technologies. It highlights key areas in agri-food manipulation, including robotic harvesting, food item manipulation, and feature evaluation, such as fruit ripeness assessment, along with the emerging field of kitchen robotics. Through this interdisciplinary exploration, we aim to inspire researchers, engineers, and practitioners to harness the power of tactile-sensing technology for transformative advancements in agri-food robotics. By providing a comprehensive understanding of the current landscape and future prospects, this review paper serves as a valuable resource for driving progress in the field of tactile sensing and its application in agri-food systems. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensor Technologies in Agri-Food)

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