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Smart Sensing, Artificial Intelligence and Robotic Solutions for Precision Horticulture,...
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Smart Sensing, Artificial Intelligence and Robotic Solutions for Precision Horticulture, Tree Ecophysiology and Phenotyping

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 9034

Special Issue Editors

Dr. Nikos Tsoulias

E-Mail Website
Guest Editor
Dept. of Agricultural Engineering, Geisenheim University, 65366 Geisenheim, Germany
Interests: agricultural engineering; biosystems engineering; robotics; precision horticulture; 3D phenotyping; fruit detection; LiDAR
Dr. Gianmarco Bortolotti

E-Mail Website
Guest Editor
Dept.of Agricultura and food Sciences – DISTAL, Univesity of Bologna, 40100 Bologna, Italy
Interests: tree-crop physiology; precision orchard management (POM); precision horticulture; robotics; computer vision; sensors; agricultural engineering
Dr. Luigi Manfrini

E-Mail Website
Guest Editor
Department of Agricultural and Food Sciences – DISTAL - Alma Mater Studiorum, University of Bologna, Viale Fanin, 46, 40127 Bologna, Italy
Interests: application of new technologies and precision management techniques; effects of the environment on fruit tree physiology; developing new management strategies to improve orchards’ sustainability, maintaining a high level of quality and yields
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, smart sensors, artificial intelligence (AI), and robotics have witnessed remarkable advancements in the agricultural sector.

These advancements primarily aim to optimize crop production while concurrently conserving resources in order to mitigate environmental impact. Smart sensing devices, such as IoT networks, aerial and terrestrial platforms, and proximal sensors, have revolutionized data collection by providing real-time spatio-temporal information on soil conditions, crop vigor, microclimate, and many other parameters. Coupled with artificial intelligence (AI), these technologies enable intelligent decision-making processes based on physiological models, enhancing agricultural practices. Together, smart sensing and AI can drive physiological-based automation in horticulture, as well as in those more complex scenario or activities.

This Issue aims to explore the intersection of smart sensing, artificial intelligence, and robotics within the context of precision horticulture, tree ecophysiology and phenotyping. We invite researchers to contribute their original works on topics including, but not limited to:

  • IoT technologies and innovative approaches for both invasive and non-invasive monitoring of tree ecophysiology including water stress, photosynthesis, and carbon assimilation, combined with smart sensing technologies for monitoring and managing plant growth, water usage, and nutrient levels in horticultural systems.
  • Artificial intelligence application for data analysis, prediction, and the optimization of management in precision horticulture.
  • Robotic solutions easing/automating in situ plant phenotyping crop monitoring, harvesting, and maintenance in orchards and greenhouses.
  • Digital twins and models for non-invasive monitoring and prediction of plant physiology
  • High-throughput phenotyping techniques using advanced machine vision technologies and machine learning/AI algorithms.

Bringing together diverse perspectives and cutting-edge research, this SI aims to foster interdisciplinary collaborations and promote the adoption of smart sensing, artificial intelligence, and robotic solutions in the field of horticultural science.

We believe that this convergence of technologies has the potential to revolutionize horticultural practices, improve crop productivity, and contribute to sustainable agricultural systems.

Dr. Nikos Tsoulias
Dr. Gianmarco Bortolotti
Dr. Luigi Manfrini
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. Plants 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 2700 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

  • sensing
  • artificial intelligence (AI)
  • robotics
  • phenotyping
  • precision horticulture
  • precision orchard management (POM)
  • IoT
  • digital twin

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Published Papers (5 papers)

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Research

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22 pages, 20682 KiB  
Article
Three-Dimensional Phenotyping Pipeline of Potted Plants Based on Neural Radiation Fields and Path Segmentation
by Xinghui Zhu, Zhongrui Huang and Bin Li
Plants 2024, 13(23), 3368; https://doi.org/10.3390/plants13233368 - 29 Nov 2024
Cited by 3 | Viewed by 869
Abstract
Precise acquisition of potted plant traits has great theoretical significance and practical value for variety selection and guiding scientific cultivation practices. Although phenotypic analysis using two dimensional(2D) digital images is simple and efficient, leaf occlusion reduces the available phenotype information. To address the [...] Read more.
Precise acquisition of potted plant traits has great theoretical significance and practical value for variety selection and guiding scientific cultivation practices. Although phenotypic analysis using two dimensional(2D) digital images is simple and efficient, leaf occlusion reduces the available phenotype information. To address the current challenge of acquiring sufficient non-destructive information from living potted plants, we proposed a three dimensional (3D) phenotyping pipeline that combines neural radiation field reconstruction with path analysis. An indoor collection system was constructed to obtain multi-view image sequences of potted plants. The structure from motion and neural radiance fields (SFM-NeRF) algorithm was then utilized to reconstruct 3D point clouds, which were subsequently denoised and calibrated. Geometric-feature-based path analysis was employed to separate stems from leaves, and density clustering methods were applied to segment the canopy leaves. Phenotypic parameters of potted plant organs were extracted, including height, stem thickness, leaf length, leaf width, and leaf area, and they were manually measured to obtain the true values. The results showed that the coefficient of determination (R2) values, indicating the correlation between the model traits and the true traits, ranged from 0.89 to 0.98, indicating a strong correlation. The reconstruction quality was good. Additionally, 22 potted plants were selected for exploratory experiments. The results indicated that the method was capable of reconstructing plants of various varieties, and the experiments identified key conditions essential for successful reconstruction. In summary, this study developed a low-cost and robust 3D phenotyping pipeline for the phenotype analysis of potted plants. This proposed pipeline not only meets daily production requirements but also advances the field of phenotype calculation for potted plants. Full article
(This article belongs to the Special Issue Smart Sensing, Artificial Intelligence and Robotic Solutions for Precision Horticulture, Tree Ecophysiology and Phenotyping)
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19 pages, 13598 KiB  
Article
Structural Parameter Optimization of a Tomato Robotic Harvesting Arm: Considering Collision-Free Operation Requirements
by Chuanlang Peng, Qingchun Feng, Zhengwei Guo, Yuhang Ma, Yajun Li, Yifan Zhang and Liangzheng Gao
Plants 2024, 13(22), 3211; https://doi.org/10.3390/plants13223211 - 15 Nov 2024
Viewed by 1144
Abstract
The current harvesting arms used in harvesting robots are developed based on standard products. Due to design constraints, they are unable to effectively avoid obstacles while harvesting tomatoes in tight spaces. To enhance the robot’s capability in obstacle-avoidance picking of tomato bunches with [...] Read more.
The current harvesting arms used in harvesting robots are developed based on standard products. Due to design constraints, they are unable to effectively avoid obstacles while harvesting tomatoes in tight spaces. To enhance the robot’s capability in obstacle-avoidance picking of tomato bunches with various postures, this study proposes a geometric parameter optimization method for a 7 degree of freedom (DOF) robotic arm. This method ensures that the robot can reach a predetermined workspace with a more compact arm configuration. The optimal picking posture for the end-effector is determined by analyzing the spatial distribution of tomato bunches, the main stem position, and peduncle posture, enabling a quantitative description of the obstacle-avoidance workspace. The denavit–hartenberg (D-H) model of the harvesting arm and the expected collision-free workspace are set as constraints. The compactness of the arm and the accessibility of the harvesting space serve as the optimization objectives. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective genetic algorithm is employed to optimize the arm length, and the results were validated through a virtual experiment using workspace traversal. The results indicate that the optimized structure of the tomato harvesting arm is compact, with a reachability of 92.88% in the workspace, based on the collision-free harvesting criteria. This study offers a reference for structural parameter optimization of robotic arms specialized in fruit and vegetable harvesting. Full article
(This article belongs to the Special Issue Smart Sensing, Artificial Intelligence and Robotic Solutions for Precision Horticulture, Tree Ecophysiology and Phenotyping)
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27 pages, 10826 KiB  
Article
CRLNet: A Multimodal Peach Detection Network Based on Cooperative Asymptotic Enhancement and the Fusion of Granularity Refinement
by Jiahao Liu, Chaoying He, Mingfang Wang, Yichu Jiang, Manman Sun, Miying Yan and Mingfang He
Plants 2024, 13(14), 1980; https://doi.org/10.3390/plants13141980 - 19 Jul 2024
Cited by 2 | Viewed by 1228
Abstract
Accurate peach detection is essential for automated agronomic management, such as mechanical peach harvesting. However, ubiquitous occlusion makes identifying peaches from complex backgrounds extremely challenging. In addition, it is difficult to capture fine-grained peach features from a single RGB image, which can suffer [...] Read more.
Accurate peach detection is essential for automated agronomic management, such as mechanical peach harvesting. However, ubiquitous occlusion makes identifying peaches from complex backgrounds extremely challenging. In addition, it is difficult to capture fine-grained peach features from a single RGB image, which can suffer from light and noise in scenarios with dense small target clusters and extreme light. To solve these problems, this study proposes a multimodal detector, called CRLNet, based on RGB and depth images. First, YOLOv9 was extended to design a backbone network that can extract RGB and depth features in parallel from an image. Second, to address the problem of information fusion bias, the Rough–Fine Hybrid Attention Fusion Module (RFAM) was designed to combine the advantageous information of different modes while suppressing the hollow noise at the edge of the peach. Finally, a Transformer-based Local–Global Joint Enhancement Module (LGEM) was developed to jointly enhance the local and global features of peaches using information from different modalities in order to enhance the percentage of information about the target peaches and remove the interference of redundant background information. CRLNet was trained on the Peach dataset and evaluated against other state-of-the-art methods; the model achieved an mAP50 of 97.1%. In addition, CRLNet also achieved an mAP50 of 92.4% in generalized experiments, validating its strong generalization capability. These results provide valuable insights for peach and other outdoor fruit multimodal detection. Full article
(This article belongs to the Special Issue Smart Sensing, Artificial Intelligence and Robotic Solutions for Precision Horticulture, Tree Ecophysiology and Phenotyping)
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18 pages, 3951 KiB  
Article
Ginkgo biloba Sex Identification Methods Using Hyperspectral Imaging and Machine Learning
by Mengyuan Chen, Chenfeng Lin, Yongqi Sun, Rui Yang, Xiangyu Lu, Weidong Lou, Xunfei Deng, Yunpeng Zhao and Fei Liu
Plants 2024, 13(11), 1501; https://doi.org/10.3390/plants13111501 - 29 May 2024
Viewed by 1793
Abstract
Ginkgo biloba L. is a rare dioecious species that is valued for its diverse applications and is cultivated globally. This study aimed to develop a rapid and effective method for determining the sex of a Ginkgo biloba. Green and yellow leaves representing [...] Read more.
Ginkgo biloba L. is a rare dioecious species that is valued for its diverse applications and is cultivated globally. This study aimed to develop a rapid and effective method for determining the sex of a Ginkgo biloba. Green and yellow leaves representing annual growth stages were scanned with a hyperspectral imager, and classification models for RGB images, spectral features, and a fusion of spectral and image features were established. Initially, a ResNet101 model classified the RGB dataset using the proportional scaling–background expansion preprocessing method, achieving an accuracy of 90.27%. Further, machine learning algorithms like support vector machine (SVM), linear discriminant analysis (LDA), and subspace discriminant analysis (SDA) were applied. Optimal results were achieved with SVM and SDA in the green leaf stage and LDA in the yellow leaf stage, with prediction accuracies of 87.35% and 98.85%, respectively. To fully utilize the optimal model, a two-stage Period-Predetermined (PP) method was proposed, and a fusion dataset was built using the spectral and image features. The overall accuracy for the prediction set was as high as 96.30%. This is the first study to establish a standard technique framework for Ginkgo sex classification using hyperspectral imaging, offering an efficient tool for industrial and ecological applications and the potential for classifying other dioecious plants. Full article
(This article belongs to the Special Issue Smart Sensing, Artificial Intelligence and Robotic Solutions for Precision Horticulture, Tree Ecophysiology and Phenotyping)
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Review

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25 pages, 18397 KiB  
Review
Advancements in Agricultural Ground Robots for Specialty Crops: An Overview of Innovations, Challenges, and Prospects
by Marcelo Rodrigues Barbosa Júnior, Regimar Garcia dos Santos, Lucas de Azevedo Sales and Luan Pereira de Oliveira
Plants 2024, 13(23), 3372; https://doi.org/10.3390/plants13233372 - 30 Nov 2024
Cited by 2 | Viewed by 3058
Abstract
Robotic technologies are affording opportunities to revolutionize the production of specialty crops (fruits, vegetables, tree nuts, and horticulture). They offer the potential to automate tasks and save inputs such as labor, fertilizer, and pesticides. Specialty crops are well known for their high economic [...] Read more.
Robotic technologies are affording opportunities to revolutionize the production of specialty crops (fruits, vegetables, tree nuts, and horticulture). They offer the potential to automate tasks and save inputs such as labor, fertilizer, and pesticides. Specialty crops are well known for their high economic value and nutritional benefits, making their production particularly impactful. While previous review papers have discussed the evolution of agricultural robots in a general agricultural context, this review uniquely focuses on their application to specialty crops, a rapidly expanding area. Therefore, we aimed to develop a state-of-the-art review to scientifically contribute to the understanding of the following: (i) the primary areas of robots’ application for specialty crops; (ii) the specific benefits they offer; (iii) their current limitations; and (iv) opportunities for future investigation. We formulated a comprehensive search strategy, leveraging Scopus® and Web of Science™ as databases and selecting “robot” and “specialty crops” as the main keywords. To follow a critical screening process, only peer-reviewed research papers were considered, resulting in the inclusion of 907 papers covering the period from 1988 to 2024. Each paper was thoroughly evaluated based on its title, abstract, keywords, methods, conclusions, and declarations. Our analysis revealed that interest in agricultural robots for specialty crops has significantly increased over the past decade, mainly driven by technological advancements in computer vision and recognition systems. Harvesting robots have arisen as the primary focus. Robots for spraying, pruning, weed control, pollination, transplanting, and fertilizing are emerging subjects to be addressed in further research and development (R&D) strategies. Ultimately, our findings serve to reveal the dynamics of agricultural robots in the world of specialty crops while supporting suitable practices for more sustainable and resilient agriculture, indicating a new era of innovation and efficiency in agriculture. Full article
(This article belongs to the Special Issue Smart Sensing, Artificial Intelligence and Robotic Solutions for Precision Horticulture, Tree Ecophysiology and Phenotyping)
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