Plant leaf segmentation for estimating phenotypic traits

Chen, Yuhao; Ribera, Javier; Boomsma, Christopher R.; Delp, Edward J.

doi:10.1109/icip.2017.8297010

Cited by 14 publications

(10 citation statements)

References 17 publications

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“…One way of performing the feature delineation is by grouping similar pixels into unique segments by meeting the criteria of dividing the image into relatively homogeneous and semantically significant groups of pixels [21,31]. In the case of vegetation, the segments can consist of crowns [23,32] or leaves [33]. Spatial patterns of the forest canopy can be captured by the texture of RGB images.…”

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confidence: 99%

Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery

et al. 2019

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Sustainable management of non-timber forest products such as palm fruits is crucial for the long-term conservation of intact forest. A major limitation to expanding sustainable management of palms has been the need for precise information about the resources at scales of tens to hundreds of hectares, while typical ground-based surveys only sample small areas. In recent years, small unmanned aerial vehicles (UAVs) have become an important tool for mapping forest areas as they are cheap and easy to transport, and they provide high spatial resolution imagery of remote areas. We developed an object-based classification workflow for RGB UAV imagery which aims to identify and delineate palm tree crowns in the tropical rainforest by combining image processing and GIS functionalities using color and textural information in an integrative way to show one of the potential uses of UAVs in tropical forests. Ten permanent forest plots with 1170 reference palm trees were assessed from October to December 2017. The results indicate that palm tree crowns could be clearly identified and, in some cases, quantified following the workflow. The best results were obtained using the random forest classifier with an 85% overall accuracy and 0.82 kappa index.

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confidence: 99%

Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery

et al. 2019

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“…Additionally, an optimal threshold value can be applied to the converted image to segment plant from background [52][53][54]. Therefore, segmenting vine canopy and minimizing shadow effect can be successfully processed with those applications of RGB camera and Hue histogram conversion [55][56][57][58]. Classification learning methods, such as K-means, artificial neural networks (ANN), random forest (RForest), and spectral indices (SI), have been applied to analyzing vines and trees, with ANN and SI methods delivering high accuracy.…”

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confidence: 99%

Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

Jang

Kim

et al. 2020

Remote Sensing

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Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

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“…As shown in Table 1 , there are two approaches for detecting or segmenting a leaf from the background: based on image segmentation techniques [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ], and based on machine learning techniques [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. In the first approach, image processing techniques such as color thresholding, superpixel (SLIC), GrabCut [ 27 ], watershed, and random walker [ 28 ] are commonly employed.…”

Section: Introductionmentioning

confidence: 99%

Plant Leaf Detection and Counting in a Greenhouse during Day and Nighttime Using a Raspberry Pi NoIR Camera

Soetedjo

Hendriarianti

2021

Sensors

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A non-destructive method using machine vision is an effective way to monitor plant growth. However, due to the lighting changes and complicated backgrounds in outdoor environments, this becomes a challenging task. In this paper, a low-cost camera system using an NoIR (no infrared filter) camera and a Raspberry Pi module is employed to detect and count the leaves of Ramie plants in a greenhouse. An infrared camera captures the images of leaves during the day and nighttime for a precise evaluation. The infrared images allow Otsu thresholding to be used for efficient leaf detection. A combination of numbers of thresholds is introduced to increase the detection performance. Two approaches, consisting of static images and image sequence methods are proposed. A watershed algorithm is then employed to separate the leaves of a plant. The experimental results show that the proposed leaf detection using static images achieves high recall, precision, and F1 score of 0.9310, 0.9053, and 0.9167, respectively, with an execution time of 551 ms. The strategy of using sequences of images increases the performances to 0.9619, 0.9505, and 0.9530, respectively, with an execution time of 516.30 ms. The proposed leaf counting achieves a difference in count (DiC) and absolute DiC (ABS_DiC) of 2.02 and 2.23, respectively, with an execution time of 545.41 ms. Moreover, the proposed method is evaluated using the benchmark image datasets, and shows that the foreground–background dice (FBD), DiC, and ABS_DIC are all within the average values of the existing techniques. The results suggest that the proposed system provides a promising method for real-time implementation.

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Plant leaf segmentation for estimating phenotypic traits

Cited by 14 publications

References 17 publications

Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery

Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery

Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

Plant Leaf Detection and Counting in a Greenhouse during Day and Nighttime Using a Raspberry Pi NoIR Camera

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