Fusion-Driven Tree Reconstruction and Fruit Localization: Advancing Precision in Agriculture

• URL: http://arxiv.org/abs/2310.15138v1
• Date: Mon, 23 Oct 2023 17:44:59 GMT
• Title: Fusion-Driven Tree Reconstruction and Fruit Localization: Advancing Precision in Agriculture
• Authors: Kaiming Fu, Peng Wei, Juan Villacres, Zhaodan Kong, Stavros G. Vougioukas, Brian N. Bailey
• Abstract summary: This study introduces an innovative methodology that harnesses the synergy of RGB imagery, LiDAR, and IMU data, to achieve intricate tree reconstructions. Experiments have been carried out in both a controlled environment and an actual peach orchard.
• Score: 2.47833893186296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fruit distribution is pivotal in shaping the future of both agriculture and agricultural robotics, paving the way for a streamlined supply chain. This study introduces an innovative methodology that harnesses the synergy of RGB imagery, LiDAR, and IMU data, to achieve intricate tree reconstructions and the pinpoint localization of fruits. Such integration not only offers insights into the fruit distribution, which enhances the precision of guidance for agricultural robotics and automation systems, but also sets the stage for simulating synthetic fruit patterns across varied tree architectures. To validate this approach, experiments have been carried out in both a controlled environment and an actual peach orchard. The results underscore the robustness and efficacy of this fusion-driven methodology, highlighting its potential as a transformative tool for future agricultural robotics and precision farming.

Related papers

• A Dataset and Benchmark for Shape Completion of Fruits for Agricultural Robotics [30.46518628656399]
  We propose the first publicly available 3D shape completion dataset for agricultural vision systems. We provide an RGB-D dataset for estimating the 3D shape of fruits.
  arXiv  Detail & Related papers  (2024-07-18T09:07:23Z)
• MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models [10.11552909915055]
  We introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances. This study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection.
  arXiv  Detail & Related papers  (2024-05-14T00:13:47Z)
• Precise Apple Detection and Localization in Orchards using YOLOv5 for Robotic Harvesting Systems [0.0]
  We propose a novel approach to apple detection and position estimation utilizing an object detection model, YOLOv5. Our results demonstrate that the YOLOv5 model outperforms its counterparts, achieving an impressive apple detection accuracy of approximately 85%.
  arXiv  Detail & Related papers  (2024-05-10T06:17:00Z)
• Few-Shot Fruit Segmentation via Transfer Learning [4.616529139444651]
  We develop a few-shot semantic segmentation framework for infield fruits using transfer learning. Motivated by similar success in urban scene parsing, we propose specialized pre-training. We show that models with pre-training learn to distinguish between fruit still on the trees and fruit that have fallen on the ground.
  arXiv  Detail & Related papers  (2024-05-04T04:05:59Z)
• Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
  This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
  arXiv  Detail & Related papers  (2024-03-27T08:42:47Z)
• Revolutionizing Agrifood Systems with Artificial Intelligence: A Survey [93.34268594812599]
  We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery. We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
  arXiv  Detail & Related papers  (2023-05-03T05:16:54Z)
• Fruit Ripeness Classification: a Survey [59.11160990637616]
  Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features.
  arXiv  Detail & Related papers  (2022-12-29T19:32:20Z)
• DeepG2P: Fusing Multi-Modal Data to Improve Crop Production [1.7406327893433846]
  We present a Natural Language Processing-based neural network architecture to process the G, E and M inputs and their interactions. We show that by modeling DNA as natural language, our approach performs better than previous approaches when tested for new environments.
  arXiv  Detail & Related papers  (2022-11-11T03:32:44Z)
• Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping [59.0626764544669]
  In this study, we use Deep Learning methods to semantically segment grapevine leaves images in order to develop an automated object detection system for leaf phenotyping. Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified.
  arXiv  Detail & Related papers  (2022-10-24T14:37:09Z)
• A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows from UAV Imagery [56.10033255997329]
  We propose a novel deep learning method based on a Convolutional Neural Network (CNN) It simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops.
  arXiv  Detail & Related papers  (2020-12-31T18:51:17Z)
• Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
  We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity. Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
  arXiv  Detail & Related papers  (2020-12-07T16:23:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.