Deep Learning-Based Direct Leaf Area Estimation using Two RGBD Datasets for Model Development

• URL: http://arxiv.org/abs/2503.10129v1
• Date: Thu, 13 Mar 2025 07:39:09 GMT
• Title: Deep Learning-Based Direct Leaf Area Estimation using Two RGBD Datasets for Model Development
• Authors: Namal Jayasuriya, Yi Guo, Wen Hu, Oula Ghannoum,
• Abstract summary: Estimation of a single leaf area can be a measure of crop growth and a phenotypic trait to breed new varieties.<n>This work investigates deep learning-based leaf area estimation, for RGBD images taken using a mobile camera setup in real-world scenarios.
• Score: 6.663132872468536
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Estimation of a single leaf area can be a measure of crop growth and a phenotypic trait to breed new varieties. It has also been used to measure leaf area index and total leaf area. Some studies have used hand-held cameras, image processing 3D reconstruction and unsupervised learning-based methods to estimate the leaf area in plant images. Deep learning works well for object detection and segmentation tasks; however, direct area estimation of objects has not been explored. This work investigates deep learning-based leaf area estimation, for RGBD images taken using a mobile camera setup in real-world scenarios. A dataset for attached leaves captured with a top angle view and a dataset for detached single leaves were collected for model development and testing. First, image processing-based area estimation was tested on manually segmented leaves. Then a Mask R-CNN-based model was investigated, and modified to accept RGBD images and to estimate the leaf area. The detached-leaf data set was then mixed with the attached-leaf plant data set to estimate the single leaf area for plant images, and another network design with two backbones was proposed: one for segmentation and the other for area estimation. Instead of trying all possibilities or random values, an agile approach was used in hyperparameter tuning. The final model was cross-validated with 5-folds and tested with two unseen datasets: detached and attached leaves. The F1 score with 90% IoA for segmentation result on unseen detached-leaf data was 1.0, while R-squared of area estimation was 0.81. For unseen plant data segmentation, the F1 score with 90% IoA was 0.59, while the R-squared score was 0.57. The research suggests using attached leaves with ground truth area to improve the results.

Related papers

• CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image [86.75098349480014]
  This paper tackles category-level pose estimation of articulated objects in robotic manipulation tasks. We propose a single-stage Network, CAP-Net, for estimating the 6D poses and sizes of Categorical Articulated Parts. We introduce the RGBD-Art dataset, the largest RGB-D articulated dataset to date, featuring RGB images and depth noise simulated from real sensors.
  arXiv  Detail & Related papers  (2025-04-15T14:30:26Z)
• LAESI: Leaf Area Estimation with Synthetic Imagery [13.145253458335464]
  We introduce LAESI, a Synthetic Leaf dataset of 100,000 synthetic leaf images on millimeter paper. This dataset provides a resource for leaf morphology analysis aimed at beech and oak leaves. We evaluate the applicability of the dataset by training machine learning models for leaf surface area prediction and semantic segmentation.
  arXiv  Detail & Related papers  (2024-03-31T07:56:07Z)
• DVMNet++: Rethinking Relative Pose Estimation for Unseen Objects [59.51874686414509]
  Existing approaches typically predict 3D translation utilizing the ground-truth object bounding box and approximate 3D rotation with a large number of discrete hypotheses. We present a Deep Voxel Matching Network (DVMNet++) that computes the relative object pose in a single pass. Our approach delivers more accurate relative pose estimates for novel objects at a lower computational cost compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-03-20T15:41:32Z)
• Eff-3DPSeg: 3D organ-level plant shoot segmentation using annotation-efficient point clouds [1.5882586857953638]
  We propose a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. High-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system. A weakly-supervised deep learning method was proposed for plant organ segmentation.
  arXiv  Detail & Related papers  (2022-12-20T14:09:37Z)
• Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming [3.95486899327898]
  We have developed a deep convolutional network that enables to predict both field and aerial images from UAVs for weed segmentation. The network learning process is visualized by feature maps at shallow and deep layers. The study shows that the developed deep convolutional neural network could be used to classify weeds from both field and aerial images.
  arXiv  Detail & Related papers  (2022-10-20T19:25:06Z)
• End-to-end deep learning for directly estimating grape yield from ground-based imagery [53.086864957064876]
  This study demonstrates the application of proximal imaging combined with deep learning for yield estimation in vineyards. Three model architectures were tested: object detection, CNN regression, and transformer models. The study showed the applicability of proximal imaging and deep learning for prediction of grapevine yield on a large scale.
  arXiv  Detail & Related papers  (2022-08-04T01:34:46Z)
• CroCo: Cross-Modal Contrastive learning for localization of Earth Observation data [62.96337162094726]
  It is of interest to localize a ground-based LiDAR point cloud on remote sensing imagery. We propose a contrastive learning-based method that trains on DEM and high-resolution optical imagery. In the best scenario, the Top-1 score of 0.71 and Top-5 score of 0.81 are obtained.
  arXiv  Detail & Related papers  (2022-04-14T15:55:00Z)
• GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER [2.8247971782279615]
  This article presents GrowliFlower, an image-based UAV time series dataset of two monitored cauliflower fields of size 0.39 and 0.60 ha acquired in 2020 and 2021. The dataset contains RGB and multispectral orthophotos from which about 14,000 individual plant coordinates are derived and provided. The dataset contains collected phenotypic traits of 740 plants, including the developmental stage as well as plant and cauliflower size.
  arXiv  Detail & Related papers  (2022-04-01T08:56:59Z)
• Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection [60.83360138070649]
  The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
  arXiv  Detail & Related papers  (2021-06-14T21:57:40Z)
• A Deep Learning Approach Based on Graphs to Detect Plantation Lines [16.76043873454695]
  We propose a deep learning approach based on graphs to detect plantation lines in UAV-based RGB imagery. The proposed method was compared against state-of-the-art deep learning methods. It achieved superior performance with a significant margin, returning precision, recall, and F1-score of 98.7%, 91.9%, and 95.1%, respectively.
  arXiv  Detail & Related papers  (2021-02-05T14:56:42Z)
• 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)
• A New Mask R-CNN Based Method for Improved Landslide Detection [54.7905160534631]
  This paper presents a novel method of landslide detection by exploiting the Mask R-CNN capability of identifying an object layout. A data set of 160 elements is created containing landslide and non-landslide images. The proposed algorithm can be potentially useful for land use planners and policy makers of hilly areas.
  arXiv  Detail & Related papers  (2020-10-04T07:46:37Z)

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.