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Journal articles on the topic 'Leaf segmentation'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Wang, Dong, Zetao Huang, Haipeng Yuan, Yun Liang, Shuqin Tu, and Cunyi Yang. "Target Soybean Leaf Segmentation Model Based on Leaf Localization and Guided Segmentation." Agriculture 13, no. 9 (2023): 1662. http://dx.doi.org/10.3390/agriculture13091662.

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The phenotypic characteristics of soybean leaves are of great significance for studying the growth status, physiological traits, and response to the environment of soybeans. The segmentation model for soybean leaves plays a crucial role in morphological analysis. However, current baseline segmentation models are unable to accurately segment leaves in soybean leaf images due to issues like leaf overlap. In this paper, we propose a target leaf segmentation model based on leaf localization and guided segmentation. The segmentation model adopts a two-stage segmentation framework. The first stage i

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Itakura, Kenta, and Fumiki Hosoi. "Automatic Leaf Segmentation for Estimating Leaf Area and Leaf Inclination Angle in 3D Plant Images." Sensors 18, no. 10 (2018): 3576. http://dx.doi.org/10.3390/s18103576.

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Automatic and efficient plant monitoring offers accurate plant management. Construction of three-dimensional (3D) models of plants and acquisition of their spatial information is an effective method for obtaining plant structural parameters. Here, 3D images of leaves constructed with multiple scenes taken from different positions were segmented automatically for the automatic retrieval of leaf areas and inclination angles. First, for the initial segmentation, leave images were viewed from the top, then leaves in the top-view images were segmented using distance transform and the watershed algo

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Wang, Peng, Hong Deng, Jiaxu Guo, et al. "Leaf Segmentation Using Modified YOLOv8-Seg Models." Life 14, no. 6 (2024): 780. http://dx.doi.org/10.3390/life14060780.

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Computer-vision-based plant leaf segmentation technology is of great significance for plant classification, monitoring of plant growth, precision agriculture, and other scientific research. In this paper, the YOLOv8-seg model was used for the automated segmentation of individual leaves in images. In order to improve the segmentation performance, we further introduced a Ghost module and a Bidirectional Feature Pyramid Network (BiFPN) module into the standard Yolov8 model and proposed two modified versions. The Ghost module can generate several intrinsic feature maps with cheap transformation op

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Et. al., Rajendra Prasad Bellapu,. "PERFORMANCE COMPARISON OF UNSUPERVISED SEGMENTATION ALGORITHMS ON RICE, GROUNDNUT, AND APPLE PLANT LEAF IMAGES." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 1090–105. http://dx.doi.org/10.17762/itii.v9i2.457.

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This paper focuses on plant leaf image segmentation by considering the aspects of various unsupervised segmentation techniques for automatic plant leaf disease detection. The segmented plant leaves are crucial in the process of automatic disease detection, quantification, and classification of plant diseases. Accurate and efficient assessment of plant diseases is required to avoid economic, social, and ecological losses. This may not be easy to achieve in practice due to multiple factors. It is challenging to segment out the affected area from the images of complex background. Thus, a robust s

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Kuo, Kuangting, Kenta Itakura, and Fumiki Hosoi. "Leaf Segmentation Based on k-Means Algorithm to Obtain Leaf Angle Distribution Using Terrestrial LiDAR." Remote Sensing 11, no. 21 (2019): 2536. http://dx.doi.org/10.3390/rs11212536.

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It is critical to take the variability of leaf angle distribution into account in a remote sensing analysis of a canopy system. Due to the physical limitations of field measurements, it is difficult to obtain leaf angles quickly and accurately, especially with a complicated canopy structure. An application of terrestrial LiDAR (Light Detection and Ranging) is a common solution for the purposes of leaf angle estimation, and it allows for the measurement and reconstruction of 3D canopy models with an arbitrary volume of leaves. However, in most cases, the leaf angle is estimated incorrectly due

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Mahajan, Vatsal, Dilip Jain, and Abhinav Dua. "Plant Leaf Segmentation Invariant of Background." International Journal of Computer & Organization Trends 12, no. 1 (2014): 24–26. http://dx.doi.org/10.14445/22492593/ijcot-v12p305.

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Ing, A., and W. Geisler. "Patch pair statistics for leaf segmentation." Journal of Vision 8, no. 6 (2010): 69. http://dx.doi.org/10.1167/8.6.69.

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Hu, Jing, Zhibo Chen, Rongguo Zhang, Meng Yang, and Shuai Zhang. "Robust random walk for leaf segmentation." IET Image Processing 14, no. 6 (2020): 1180–86. http://dx.doi.org/10.1049/iet-ipr.2018.6255.

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Li, Lei, Wenzheng Hu, Jiang Lu, and Changshui Zhang. "Leaf vein segmentation with self-supervision." Computers and Electronics in Agriculture 203 (December 2022): 107352. http://dx.doi.org/10.1016/j.compag.2022.107352.

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Mohammed Amean, Zainab, Tobias Low, and Nigel Hancock. "Automatic leaf segmentation and overlapping leaf separation using stereo vision." Array 12 (December 2021): 100099. http://dx.doi.org/10.1016/j.array.2021.100099.

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11

Wang, Yunlong, and Zhiyong Zhang. "Segment Any Leaf 3D: A Zero-Shot 3D Leaf Instance Segmentation Method Based on Multi-View Images." Sensors 25, no. 2 (2025): 526. https://doi.org/10.3390/s25020526.

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Exploring the relationships between plant phenotypes and genetic information requires advanced phenotypic analysis techniques for precise characterization. However, the diversity and variability of plant morphology challenge existing methods, which often fail to generalize across species and require extensive annotated data, especially for 3D datasets. This paper proposes a zero-shot 3D leaf instance segmentation method using RGB sensors. It extends the 2D segmentation model SAM (Segment Anything Model) to 3D through a multi-view strategy. RGB image sequences captured from multiple viewpoints

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Renner, Matt A. M., and Joshua Salter. "Unique shoot architecture in the leafy liverwort Herzogianthus vaginatus (Herzogianthaceae): insights into novel growth patterns in Jungermanniidae." Botanical Journal of the Linnean Society 193, no. 2 (2020): 207–27. http://dx.doi.org/10.1093/botlinnean/boaa012.

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Abstract All studied leafy liverworts have shoots with leaf to underleaf ratios of either 2:1 or 1:1. These ratios are the product of growth by either helical or pendular segmentation of the tetrahedral apical cell. Here we report that Herzogianthus vaginatus has a leaf to underleaf ratio approaching 3:1 on primary shoots, and on secondary shoots the ratio is closer to 4:1. These ratios are incompatible with the simple helical or pendular growth patterns found in other leafy liverworts. Further, the sequence of leaves and underleaves on Herzogianthus shoots is not wholly regular, which is prev

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Chen, Qingda, Tian Gao, Jiaojun Zhu, et al. "Individual Tree Segmentation and Tree Height Estimation Using Leaf-Off and Leaf-On UAV-LiDAR Data in Dense Deciduous Forests." Remote Sensing 14, no. 12 (2022): 2787. http://dx.doi.org/10.3390/rs14122787.

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Accurate individual tree segmentation (ITS) is fundamental to forest management and to the studies of forest ecosystem. Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) shows advantages for ITS and tree height estimation at stand and landscape scale. However, dense deciduous forests with tightly interlocked tree crowns challenge the performance for ITS. Available LiDAR points through tree crown and appropriate algorithm are expected to attack the problem. In this study, a new UAV-LiDAR dataset that fused leaf-off and leaf-on point cloud (FULD) was introduced to assess the synerg

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Khan, Ameer Tamoor, and Signe Marie Jensen. "LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11." Agriculture 15, no. 2 (2025): 196. https://doi.org/10.3390/agriculture15020196.

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Accurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture. This study evaluates YOLOv11-based models for automated leaf detection and segmentation across spring barley, spring wheat, winter wheat, winter rye, and winter triticale. The key focus is assessing whether a unified model trained on a combined multi-crop dataset can outperform crop-specific models. Results show that the unified model achieves superior performance in bounding box tasks, with mAP@50 exceeding 0.85 for spring crops and 0.7 for winter crops. Segmenta

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Manek, Patricia Gertrudis, Budiman Baso, Kristoforus Fallo, Risald Risald, and Hevi Herlina Ullu. "Segmentasi Daun Cendana Berbasis Citra Menggunakan Otsu Thresholding." Journal of Information and Technology 3, no. 1 (2023): 6–10. http://dx.doi.org/10.32938/jitu.v3i1.3868.

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The segmentation process is the separation of parts of the object area from the background in an image, so that segmented objects can be processed for other purposes such as pattern recognition. The results of segmentation must be accurate, if it is not accurate in separating objects in the image it will affect the results of further processing. The segmentation process is carried out using the Otsu Thresholding method on sandalwood leaf images by first applying the Median filter to reduce noise. After obtaining the segmented image, then performing performance measurements. The segmentation re

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Cheng, Zhihan, and He Yan. "MSFUnet: A Semantic Segmentation Network for Crop Leaf Growth Status Monitoring." AgriEngineering 7, no. 7 (2025): 238. https://doi.org/10.3390/agriengineering7070238.

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Monitoring the growth status of crop leaves is an integral part of agricultural management and involves important tasks such as leaf shape analysis and area calculation. To achieve this goal, accurate leaf segmentation is a critical step. However, this task presents a challenge, as crop leaf images often feature substantial overlap, obstructing the precise differentiation of individual leaf edges. Moreover, existing segmentation methods fail to preserve fine edge details, a deficiency that compromises precise morphological analysis. To overcome these challenges, we introduce MSFUnet, an innova

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Ammar, Mohamed. "Enhancing real-time instance segmentation for plant disease detection with improved YOLOv8-Seg algorithm." International Journal on Information Technologies and Security 16, no. 2 (2024): 27–38. http://dx.doi.org/10.59035/bcnl3199.

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With widespread uses in areas as diverse as traffic analysis and medical imaging, picture segmentation is a basic problem in computer vision. Instance segmentation, which combines object recognition with segmentation, is a powerful tool for item identification and exact delineation. Using the Tomato Leaf disease dataset as an example, this research delves into the topic of segmentation training by capitalizing on the simplicity of enhanced YOLOv8-Seg models. Tomato leaf disease are the focus of this instance-segmentation dataset, which seeks to resolve the pressing problem of agricultural diff

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Qian, Tingting, Yangxin Liu, Shenglian Lu, et al. "Cucumber Leaf Segmentation Based on Bilayer Convolutional Network." Agronomy 14, no. 11 (2024): 2664. http://dx.doi.org/10.3390/agronomy14112664.

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When monitoring crop growth using top-down images of the plant canopies, leaves in agricultural fields appear very dense and significantly overlap each other. Moreover, the image can be affected by external conditions such as background environment and light intensity, impacting the effectiveness of image segmentation. To address the challenge of segmenting dense and overlapping plant leaves under natural lighting conditions, this study employed a Bilayer Convolutional Network (BCNet) method for accurate leaf segmentation across various lighting environments. The major contributions of this st

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Cao, Liying, Hongda Li, Helong Yu, Guifen Chen, and Heshu Wang. "Plant Leaf Segmentation and Phenotypic Analysis Based on Fully Convolutional Neural Network." Applied Engineering in Agriculture 37, no. 5 (2021): 929–40. http://dx.doi.org/10.13031/aea.14495.

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HighlightsModify the U-Net segmentation network to reduce the loss of segmentation accuracy.Reducing the number of layers U-net network, modifying the loss function, and the increase in the output layer dropout.It can be well extracted after splitting blade morphological model and color feature.Abstract. From the perspective of computer vision, the shortcut to extract phenotypic information from a single crop in the field is image segmentation. Plant segmentation is affected by the background environment and illumination. Using deep learning technology to combine depth maps with multi-view ima

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Cai, Maodong, Xiaomei Yi, Guoying Wang, et al. "Image Segmentation Method for Sweetgum Leaf Spots Based on an Improved DeeplabV3+ Network." Forests 13, no. 12 (2022): 2095. http://dx.doi.org/10.3390/f13122095.

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This paper discusses a sweetgum leaf-spot image segmentation method based on an improved DeeplabV3+ network to address the low accuracy in plant leaf spot segmentation, problems with the recognition model, insufficient datasets, and slow training speeds. We replaced the backbone feature extraction network of the model's encoder with the MobileNetV2 network, which greatly reduced the amount of calculation being performed in the model and improved its calculation speed. Then, the attention mechanism module was introduced into the backbone feature extraction network and the decoder, which further

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Yang, Tingting, Suyin Zhou, Aijun Xu, Junhua Ye, and Jianxin Yin. "An Approach for Plant Leaf Image Segmentation Based on YOLOV8 and the Improved DEEPLABV3+." Plants 12, no. 19 (2023): 3438. http://dx.doi.org/10.3390/plants12193438.

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Accurate plant leaf image segmentation provides an effective basis for automatic leaf area estimation, species identification, and plant disease and pest monitoring. In this paper, based on our previous publicly available leaf dataset, an approach that fuses YOLOv8 and improved DeepLabv3+ is proposed for precise image segmentation of individual leaves. First, the leaf object detection algorithm-based YOLOv8 was introduced to reduce the interference of backgrounds on the second stage leaf segmentation task. Then, an improved DeepLabv3+ leaf segmentation method was proposed to more efficiently c

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Storcz, Tamás, Zsolt Ercsey, and Géza Várady. "Histogram based segmentation of shadowed leaf images." Pollack Periodica 13, no. 1 (2018): 21–32. http://dx.doi.org/10.1556/606.2018.13.1.2.

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23

Udawant, Prashant, and Pravin Srinath. "Cotton Leaf Disease Detection Using Instance Segmentation." Journal of Cases on Information Technology 24, no. 4 (2022): 1–10. http://dx.doi.org/10.4018/jcit.296721.

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Cotton is one of the most important cash and fiber crops in India. Agricultural machine learning plays a very important role in this agricultural industry. In this paper, the use of an object detection algorithm namely Mask RCNN along with transfer learning is experimented to find out if it is a fit algorithm to detect cotton leaf diseases in practical situations. The model training accuracy is found as 94 % whereas total loss value is continuously decreasing as number of optimize iterations are increasing.

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Yang, Ruotong, Yaojiang Guo, Zhiwei Hu, Ruibo Gao, and Hua Yang. "Semantic Segmentation of Cucumber Leaf Disease Spots Based on ECA-SegFormer." Agriculture 13, no. 8 (2023): 1513. http://dx.doi.org/10.3390/agriculture13081513.

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Accurate semantic segmentation of disease spots is critical in the evaluation and treatment of cucumber leaf damage. To solve the problem of poor segmentation accuracy caused by the imbalanced feature fusion of SegFormer, the Efficient Channel Attention SegFormer (ECA-SegFormer) is proposed to handle the semantic segmentation of cucumber leaf disease spots under natural acquisition conditions. First, the decoder of SegFormer is modified by inserting the Efficient Channel Attention and adopting the Feature Pyramid Network to increase the scale robustness of the feature representation. Then, a c

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Fu, Jun, Yichen Zhao, and Gang Wu. "Potato Leaf Disease Segmentation Method Based on Improved UNet." Applied Sciences 13, no. 20 (2023): 11179. http://dx.doi.org/10.3390/app132011179.

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The precise control of potato diseases is an urgent demand in smart agriculture, with one of the key aspects being the accurate identification and segmentation of potato leaf diseases. Some disease spots on potato leaves are relatively small, and to address issues such as information loss and low segmentation accuracy in the process of potato leaf disease image segmentation, a novel approach based on an improved UNet network model is proposed. Firstly, the incorporation of ResNet50 as the backbone network is introduced to deepen the network structure, effectively addressing problems like gradi

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Mohana Priya. C, Et al. "Customized Semantic Segmentation for Enhanced Disease Detection of Maize Leaf Images." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2023): 31–37. http://dx.doi.org/10.17762/ijritcc.v11i11.9074.

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Maize leaf images are affected by various diseases. Though many image processing techniques are available to identify diseased segment of a diseased maize leaf image proper methodology to segment every chunk in the leaf as disease, shadow, healthy and background using a single methodology is still in search of. So, a single line of attack is availed using Semantic Segmentation for diseased maize Leaf images through which every pixel in an image is equated to a class. Initially multiple classes in the maize leaf images are Labeled and trained. ImagedataStore and PixelLabelDatastore are used to

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Zhang, Lili, and Xiyin Liang. "Image Segmentation of Plant Leaves in Natural Environments Based on LinkNet." Journal of Computing and Electronic Information Management 11, no. 3 (2023): 67–72. http://dx.doi.org/10.54097/jceim.v11i3.15.

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The conventional approach to plant leaf image segmentation for subsequent measurement of leaf geometric parameters, while reasonably accurate, exhibits lower efficiency. To address this challenge, a plant leaf image segmentation algorithm based on deep learning semantic segmentation models and transfer learning is proposed to achieve rapid and precise leaf segmentation. The presented method adopts LinkNet as its foundational network structure and introduces four key enhancements: leveraging ResNet18 as the backbone encoder network to expedite model fitting through transfer learning; reducing t

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Kalaivani, S., S. P. Shantharajah, and T. Padma. "Agricultural leaf blight disease segmentation using indices based histogram intensity segmentation approach." Multimedia Tools and Applications 79, no. 13-14 (2019): 9145–59. http://dx.doi.org/10.1007/s11042-018-7126-7.

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Liu, Li Bo. "Research on the Segmentation Method of Rice Leaf Disease Image." Applied Mechanics and Materials 220-223 (November 2012): 1339–44. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1339.

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In Order to Improve the Segmentation Effect of the Rice Leaf Disease Images, we Take Optimal Iterative Threshold Method,OTSU Method and Fuzzy C-means Clustering Algorithm to Make Adaptive Segmentation of Rice Disease Images which Were Collected under Different Circumstances. through Comparative Analysis, Experimental Results Show that: Three Methods All Can Effective Separate Spot from the Leaves; in Comparison, the Effect of the Fuzzy C-means Clustering Algorithm Is the Best, but the Number of Iterations Is too many and the Time Spent on it Is the Most; the Effect of OTSU Method Is Lesser, Op

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Bhujbal, Sahil, Pradnya Mandale, Vaishnavi Aher, and Rushikesh Wable. "Soybean Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1128–32. http://dx.doi.org/10.22214/ijraset.2023.47611.

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Abstract: With the continuous integration of computer technology into agricultural production, it also reduces personnel costs while improving agricultural production efficiency and quality. Crop disease control is an important part of agricultural production, and the use of computer vision technology to quickly and accurately identify crop diseases is an important means of ensuring a good harvest of agricultural products and promoting agricultural modernization. In this paper, a recognition method based on deep learning is proposed based on soybean brown spot. The method is divided into image

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Lv, Jidong, and Liming Xu. "Method to acquire regions of fruit, branch and leaf from image of red apple in orchard." Modern Physics Letters B 31, no. 19-21 (2017): 1740039. http://dx.doi.org/10.1142/s0217984917400395.

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This work proposed a method to acquire regions of fruit, branch and leaf from red apple image in orchard. To acquire fruit image, R-G image was extracted from the RGB image for corrosive working, hole filling, subregion removal, expansive working and opening operation in order. Finally, fruit image was acquired by threshold segmentation. To acquire leaf image, fruit image was subtracted from RGB image before extracting 2G-R-B image. Then, leaf image was acquired by subregion removal and threshold segmentation. To acquire branch image, dynamic threshold segmentation was conducted in the R-G ima

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Zhao, Wenbo, Lijun Hu, Qi Wang, et al. "RMP-UNet: An Efficient and Lightweight Model for Apple Leaf Disease Segmentation." Agronomy 15, no. 4 (2025): 770. https://doi.org/10.3390/agronomy15040770.

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As an important and nutrient-rich economic crop, apple is significantly threatened by leaf diseases, which severely impact yield, making the timely and accurate diagnosis and segmentation of these diseases crucial. Traditional segmentation models face challenges such as low segmentation accuracy and excessive model size, limiting their applicability on resource-constrained devices. To address these issues, this study proposes RMP-UNet, an efficient and lightweight model for apple leaf disease segmentation. Based on the traditional UNet architecture, RMP-UNet incorporates an efficient multi-sca

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Li, Jie, Li Qingqing, Jiangwei Qiao, Li Li, Jian Yao, and Jingmin Tu. "Organ-Level Instance Segmentation of Oilseed Rape at Seedling Stage Based on 3D Point Cloud." Applied Engineering in Agriculture 40, no. 2 (2024): 151–64. http://dx.doi.org/10.13031/aea.15698.

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Highlights Provides a low-cost, fast, accurate, and non-destructive method for segmenting stems and leaves of oilseed rape at the seedling stage. Different varieties and leaf shapes of oilseed rape can all be accurately and efficiently segmented at organ level. The results of accurate organ instance segmentation are used for further organ morphological structure analysis and organ-level phenotype extraction. The calculated phenotypic data is highly correlated with the true phenotypic data. Abstract. Organ-level plant point cloud instance segmentation is crucial for three-dimensional (3D) plant

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Chen, Shuo, Kefei Zhang, Yindi Zhao, et al. "An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation." Agriculture 11, no. 5 (2021): 420. http://dx.doi.org/10.3390/agriculture11050420.

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Rice bacterial leaf streak (BLS) is a serious disease in rice leaves and can seriously affect the quality and quantity of rice growth. Automatic estimation of disease severity is a crucial requirement in agricultural production. To address this, a new method (termed BLSNet) was proposed for rice and BLS leaf lesion recognition and segmentation based on a UNet network in semantic segmentation. An attention mechanism and multi-scale extraction integration were used in BLSNet to improve the accuracy of lesion segmentation. We compared the performance of the proposed network with that of DeepLabv3

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Paithane, Pradip, and Sarita Jibhau Wagh. "Novel modified kernel fuzzy c-means algorithm used for cotton leaf spot detection." System research and information technologies, no. 4 (December 26, 2023): 85–99. http://dx.doi.org/10.20535/srit.2308-8893.2023.4.07.

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Image segmentation is a significant and difficult subject that is a prerequisite for both basic image analysis and sophisticated picture interpretation. In image analysis, picture segmentation is crucial. Several different applications, including those related to medicine, facial identification, Cotton disease diagnosis, and map object detection, benefit from image segmentation. In order to segment images, the clustering approach is used. The two types of clustering algorithms are Crisp and Fuzzy. Crisp clustering is superior to fuzzy clustering. Fuzzy clustering uses the well-known FCM approa

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Muthukannan, Kanthan, and Pitchai Latha. "A PSO MODEL FOR DISEASE PATTERN DETECTION ON LEAF SURFACES." Image Analysis & Stereology 34, no. 3 (2015): 209. http://dx.doi.org/10.5566/ias.1227.

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The main objective of this paper is to segment the disease affected portion of a plant leaf and extract the hybrid features for better classification of different disease patterns. A new approach named as Particle Swarm Optimization (PSO) is proposed for image segmentation. PSO is an automatic unsupervised efficient algorithm which is used for better segmentation and better feature extraction. Features extracted after segmentation are important for disease classification so that the hybrid feature extraction components controls the accuracy of classification for different diseases. The approac

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Huang, Fei, Yanming Li, Zixiang Liu, Liang Gong, and Chengliang Liu. "A Method for Calculating the Leaf Area of Pak Choi Based on an Improved Mask R-CNN." Agriculture 14, no. 1 (2024): 101. http://dx.doi.org/10.3390/agriculture14010101.

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The leaf area of pak choi is a critical indicator of growth rate, nutrient absorption, and photosynthetic efficiency, and it is required to be precisely measured for an optimal agricultural output. Traditional methods often fail to deliver the necessary accuracy and efficiency. We propose a method for calculating the leaf area of pak choi based on an improved Mask R-CNN. We have enhanced Mask R-CNN by integrating an advanced attention mechanism and a two-layer fully convolutional network (FCN) into its segmentation branch. This integration significantly improves the model’s ability to detect a

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Li, Dawei, Yan Cao, Xue-song Tang, Siyuan Yan, and Xin Cai. "Leaf Segmentation on Dense Plant Point Clouds with Facet Region Growing." Sensors 18, no. 11 (2018): 3625. http://dx.doi.org/10.3390/s18113625.

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Leaves account for the largest proportion of all organ areas for most kinds of plants, and are comprise the main part of the photosynthetically active material in a plant. Observation of individual leaves can help to recognize their growth status and measure complex phenotypic traits. Current image-based leaf segmentation methods have problems due to highly restricted species and vulnerability toward canopy occlusion. In this work, we propose an individual leaf segmentation approach for dense plant point clouds using facet over-segmentation and facet region growing. The approach can be divided

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Vishnu Prabhakar. V, Dr. N. Sudha. "Segmentation Algorithms For Accurate Decision Of Banana Leaf Diseases In Precision Agriculture." Nanotechnology Perceptions 20, no. 4 (2024): 806–16. https://doi.org/10.62441/nano-ntp.v20i4.5270.

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Banana cultivation plays a crucial role in the agricultural economy, but its productivity is significantly affected by various leaf diseases. Early and accurate identification of banana leaf diseases is essential for effective disease management and yield optimization. This study presents an automated approach for detecting and classifying banana leaf diseases using a segmentation algorithm. The proposed method involves image preprocessing, segmentation, and feature extraction techniques to isolate diseased regions from healthy leaf areas. By applying advanced image processing and machine lear

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Wang, Guowei, Jiawei Wang, Jiaxin Wang, and Yadong Sun. "Grape Leaf Disease Classification Combined with U-Net++ Network and Threshold Segmentation." Computational Intelligence and Neuroscience 2022 (October 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/1042737.

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Applying the method of semantic segmentation to the segmentation of grape leaves is an important method to solve how to segment grape leaves from complex backgrounds. This article uses U-net++ convolutional neural network to segment grape leaves from complex backgrounds using MIOU, PA, and mPA as evaluation metrics. After the leaves are segmented, the OTSU threshold segmentation + EXG algorithm is used to extract the diseased spots of grape leaves and healthy grape leaves by increasing the proportion of green vectors. Grape leaf disease was automatically graded by the ratio of the healthy gree

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Liu, Yunling, Guoli Zhang, Ke Shao, et al. "Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud." Agronomy 12, no. 4 (2022): 893. http://dx.doi.org/10.3390/agronomy12040893.

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Accurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we used the SFM algorithm to reconstruct the 3D point clouds from multi-view 2D images and obtained the sugar beet plant point clouds after preprocessing. We then segmented them using the multiscale tensor voting method (MSTVM)-based region-growing algorithm, resulting in independent leaves and overlapping l

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Li, Haitao, Gengchen Wu, Shutian Tao, et al. "Automatic Branch–Leaf Segmentation and Leaf Phenotypic Parameter Estimation of Pear Trees Based on Three-Dimensional Point Clouds." Sensors 23, no. 9 (2023): 4572. http://dx.doi.org/10.3390/s23094572.

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The leaf phenotypic traits of plants have a significant impact on the efficiency of canopy photosynthesis. However, traditional methods such as destructive sampling will hinder the continuous monitoring of plant growth, while manual measurements in the field are both time-consuming and laborious. Nondestructive and accurate measurements of leaf phenotypic parameters can be achieved through the use of 3D canopy models and object segmentation techniques. This paper proposed an automatic branch–leaf segmentation pipeline based on lidar point cloud and conducted the automatic measurement of leaf i

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Wang, Haoyu, Jie Ding, Sifan He, et al. "MFBP-UNet: A Network for Pear Leaf Disease Segmentation in Natural Agricultural Environments." Plants 12, no. 18 (2023): 3209. http://dx.doi.org/10.3390/plants12183209.

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The accurate prevention and control of pear tree diseases, especially the precise segmentation of leaf diseases, poses a serious challenge to fruit farmers globally. Given the possibility of disease areas being minute with ambiguous boundaries, accurate segmentation becomes difficult. In this study, we propose a pear leaf disease segmentation model named MFBP-UNet. It is based on the UNet network architecture and integrates a Multi-scale Feature Extraction (MFE) module and a Tokenized Multilayer Perceptron (BATok-MLP) module with dynamic sparse attention. The MFE enhances the extraction of det

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Deenan, Suryaprabha, Satheeshkumar Janakiraman, and Seenivasan Nagachandrabose. "Image Segmentation Algorithms for Banana Leaf Disease Diagnosis." Journal of The Institution of Engineers (India): Series C 101, no. 5 (2020): 807–20. http://dx.doi.org/10.1007/s40032-020-00592-5.

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Scharr, Hanno, Massimo Minervini, Andrew P. French, et al. "Leaf segmentation in plant phenotyping: a collation study." Machine Vision and Applications 27, no. 4 (2015): 585–606. http://dx.doi.org/10.1007/s00138-015-0737-3.

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Natesan, Balaji, Anandakumar Singaravelan, Jia-Lien Hsu, Yi-Hsien Lin, Baiying Lei, and Chuan-Ming Liu. "Channel–Spatial Segmentation Network for Classifying Leaf Diseases." Agriculture 12, no. 11 (2022): 1886. http://dx.doi.org/10.3390/agriculture12111886.

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Agriculture is an important resource for the global economy, while plant disease causes devastating yield loss. To control plant disease, every country around the world spends trillions of dollars on disease management. Some of the recent solutions are based on the utilization of computer vision techniques in plant science which helps to monitor crop industries such as tomato, maize, grape, citrus, potato and cassava, and other crops. The attention-based CNN network has become effective in plant disease prediction. However, existing approaches are less precise in detecting minute-scale disease

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Wahjuni, Sri, Wulandari, and Husna Nurarifah. "Faster RCNN based leaf segmentation using stereo images." Journal of Agriculture and Food Research 11 (March 2023): 100514. http://dx.doi.org/10.1016/j.jafr.2023.100514.

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Tang, Wenkang, Bibo Lu, Peipei Zhou, Jie Yang, and Aiqing Song. "MAU-Net: Full-period Mango Leaf Disease Image Segmentation Algorithm Based on an Improved UNet Network." Journal of Research in Science and Engineering 7, no. 1 (2025): 111–24. https://doi.org/10.53469/jrse.2025.07(01).18.

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Mango leaf disease segmentation is an essential foundation for accurate disease diagnosis and intelligent grading. The size and shape of mango leaf diseases vary significantly at different times, making it difficult for mainstream semantic segmentation methods to segment disease areas accurately. Therefore, this paper proposes a method called MAU-Net for fine segmentation of mango leaf diseases over the whole period. The MAU-Net is based on the traditional Unet architecture, integrates the Self-Aligning Attention Feature Fusion (SAFF) module and the Multiscale Feature Enhancement (MFE) module,

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Jeong, Seongho, Seongkyun Jeong, and Jaehwan Bong. "Detection of Tomato Leaf Miner Using Deep Neural Network." Sensors 22, no. 24 (2022): 9959. http://dx.doi.org/10.3390/s22249959.

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As a result of climate change and global warming, plant diseases and pests are drawing attention because they are dispersing more quickly than ever before. The tomato leaf miner destroys the growth structure of the tomato, resulting in 80 to 100 percent tomato loss. Despite extensive efforts to prevent its spread, the tomato leaf miner can be found on most continents. To protect tomatoes from the tomato leaf miner, inspections must be performed on a regular basis throughout the tomato life cycle. To find a better deep neural network (DNN) approach for detecting tomato leaf miner, we investigat

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Yao, Hui, Chunshan Wang, Lijie Zhang, Jiuxi Li, Bo Liu, and Fangfang Liang. "A Cucumber Leaf Disease Severity Grading Method in Natural Environment Based on the Fusion of TRNet and U-Net." Agronomy 14, no. 1 (2023): 72. http://dx.doi.org/10.3390/agronomy14010072.

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Disease severity grading is the primary decision-making basis for the amount of pesticide usage in vegetable disease prevention and control. Based on deep learning, this paper proposed an integrated framework, which automatically segments the target leaf and disease spots in cucumber images using different semantic segmentation networks and then calculates the area of disease spots and the target leaf for disease severity grading. Two independent datasets of leaves and lesions were constructed, which served as the training set for the first-stage diseased leaf segmentation and the second-stage

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