Leaf Segments Of Plants Research Articles

Leaf Segmentation Using Modified YOLOv8-Seg Models.

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 operations, and the BiFPN module can fuse multi-scale features to improve the segmentation performance of small leaves. The experiment results show that Yolov8 performs well in the leaf segmentation task, and the Ghost module and BiFPN module can further improve the performance. Our proposed approach achieves a 86.4% leaf segmentation score (best Dice) over all five test datasets of the Computer Vision Problems in Plant Phenotyping (CVPPP) Leaf Segmentation Challenge, outperforming other reported approaches.

Optimized deep learning network for plant leaf disease segmentation and multi-classification using leaf images

ABSTRACT Automatic detection of plant diseases is very imperative for monitoring the plants because they are one of the major concerns in the agricultural sector. Continuous monitoring can combat diseases of plants, which contribute to production loss. In the global production of agricultural goods, the disease of plants plays a significant role and harms yield, resulting in losses for the economy, society, and environment. It seems like a difficult and time-consuming task to manually identify diseased symptoms on leaves. The majority of disease symptoms are reflected in plant leaves, but experts in laboratories spend a lot of money and time diagnosing them. The majority of the features, which affect crop superiority and amount are plant or crop diseases. Therefore, classification, segmentation, and recognition of contaminated symptoms at the starting phase of infection is indispensable. Precision agriculture employs a deep learning model to jointly address these issues. In this research, an efficient disease of plant leaf segmentation and plant leaf disease recognition model is introduced using an optimized deep learning technique. As a result, maximum testing accuracy of 94.69%, sensitivity of 95.58%, and specificity of 92.90% were attained by the optimized deep learning method.

Maize emergence rate and leaf emergence speed estimation via image detection under field rail-based phenotyping platform

Accurate and efficient acquisition of maize emergence rate and leaf emergence speed in the field is essential for detecting seed quality, evaluating crop field management plans, and yield assessment. This study constructs a system solution to obtain the maize seedling emergence rate and leaf emergence speed based on the field rail-based phenotyping platform and convolutional neural network. Firstly, we use the field rail-based phenotyping platform to collect a high-temporal sequence visible light images of maize plant during the seedling stage. In the first stage, an improved Faster R-CNN is used to detect maize seedlings in the plot images, and the plant ROI area is cropped as the input for the second stage network. In the second stage, the best performing ResNeSt network out of four backbone networks is chosen, using the Mask R-CNN model to segment the leaves of the input plant image, which is then used to calculate the number of leaves. We propose a quantification index for leaf emergence speed based on a weighted average combination of leaf numbers. Using the method described in this paper, we analyzed the plant images from 52 inbred lines plots of over seven consecutive days. The experimental results show that when the Intersection Over Union (IOU) is 0.50, the bbox_mAP of the maize seedling detection model is 0.969, with an accuracy rate of 99.53%. Compared with manual counting, the calculated R2 is 0.997 and RMSE is 43.382. The segm_mAP of the plant leaf segmentation model is 0.942. The differences in emergence rate and leaf emergence speed across 52 inbred lines were compared, providing new phenotyping reference indices for further exploring the genotypic differences affecting seed emergence and leafing.

Local refinement mechanism for improved plant leaf segmentation in cluttered backgrounds.

Plant phenotyping is a critical field in agriculture, aiming to understand crop growth under specific conditions. Recent research uses images to describe plant characteristics by detecting visual information within organs such as leaves, flowers, stems, and fruits. However, processing data in real field conditions, with challenges such as image blurring and occlusion, requires improvement. This paper proposes a deep learning-based approach for leaf instance segmentation with a local refinement mechanism to enhance performance in cluttered backgrounds. The refinement mechanism employs Gaussian low-pass and High-boost filters to enhance target instances and can be applied to the training or testing dataset. An instance segmentation architecture generates segmented masks and detected areas, facilitating the derivation of phenotypic information, such as leaf count and size. Experimental results on a tomato leaf dataset demonstrate the system's accuracy in segmenting target leaves despite complex backgrounds. The investigation of the refinement mechanism with different kernel sizes reveals that larger kernel sizes benefit the system's ability to generate more leaf instances when using a High-boost filter, while prediction performance decays with larger Gaussian low-pass filter kernel sizes. This research addresses challenges in real greenhouse scenarios and enables automatic recognition of phenotypic data for smart agriculture. The proposed approach has the potential to enhance agricultural practices, ultimately leading to improved crop yields and productivity.

Cotton leaf segmentation with composite backbone architecture combining convolution and attention.

Plant leaf segmentation, especially leaf edge accurate recognition, is the data support for automatically measuring plant phenotypic parameters. However, adjusting the backbone in the current cutting-edge segmentation model for cotton leaf segmentation applications requires various trial and error costs (e.g., expert experience and computing costs). Thus, a simple and effective semantic segmentation architecture (our model) based on the composite backbone was proposed, considering the computational requirements of the mainstream Transformer backbone integrating attention mechanism. The composite backbone was composed of CoAtNet and Xception. CoAtNet integrated the attention mechanism of the Transformers into the convolution operation. The experimental results showed that our model outperformed the benchmark segmentation models PSPNet, DANet, CPNet, and DeepLab v3+ on the cotton leaf dataset, especially on the leaf edge segmentation (MIoU: 0.940, BIoU: 0.608). The composite backbone of our model integrated the convolution of the convolutional neural networks and the attention of the Transformers, which alleviated the computing power requirements of the Transformers under excellent performance. Our model reduces the trial and error cost of adjusting the segmentation model architecture for specific agricultural applications and provides a potential scheme for high-throughput phenotypic feature detection of plants.

Eff-UNet++: A novel architecture for plant leaf segmentation and counting

Leaf segmentation learns more about leaf-level traits such as leaf area, count, stress, and development phases. In plant phenotyping, segmentation and counting of plant organs like leaves are a major challenge due to considerable overlap between leaves and varying environmental conditions, including brightness variation and shadow, blur due to wind. Further, the plant's inherent challenges, such as the leaf texture, genotype, size, shape, and density variation of leaves, make the leaf segmentation task more complex. To meet these challenges, the present work proposes a novel method for leaf segmentation and counting employing Eff-Unet++, an encoder-decoder-based architecture. This architecture uses EfficientNet-B4 as an encoder for accurate feature extraction. The redesigned skip connections and residual block in the decoder utilize encoder output and help to address the information degradation problem. In addition, the redesigned skip connections reduce the computational complexity. The lateral output layer is introduced to aggregate the low-level to high-level features from the decoder, which improves segmentation performance. The proposed method validates its performance on three datasets: KOMATSUNA dataset, Multi-Modality Plant Imagery Dataset (MSU-PID), and Computer Vision for Plant Phenotyping dataset (CVPPP). The proposed approach outperforms the existing state-of-the-art methods UNet, UNet++, Residual-UNet, InceptionResv2-UNet, and DeeplabV3 leaf segmentation results achieve best dice (BestDice): 83.44, 71.17, 78.27 and Foreground-Background Dice (FgBgDice): 97.48, 91.35, 96.38 on KOMATSUNA, MSU-PID, and CVPPP dataset respectively. In addition, for leaf counting the results are difference in count (DiffFG): 0.11, 0.03, 0.12 and Absolute Difference in count (AbsDiffFG): 0.21, 0.38, 1.27 on KOMATSUNA, MSU-PID, and CVPPP dataset respectively.

Image-based automatic segmentation of leaf using clustering algorithm

This paper proposes a productive strategy to separate the leaf area from different plant images. The image analysis in plants plays a critical job in reasonable and gainful farming. Plant image analysis is mainly used to record the plant development plant area and leaf zone etc. Plant leaf segmentation has become a significant task to be examined among these plant characters. For this reason, a strategy for leaf area segmentation from different plant images is made. In this paper we have proposed an algorithm which will segment plant on the basis of L*a*b colour spaces and cluster formation. When our proposed technique is compared to rosette tracker it was found that our proposed algorithm was more efficient in plant leaf segmentation. The results of our proposed algorithm are, accuracy is 79.23%, sensitivity is 85.84%, and specificity is 97.40%.

PLANT LEAF SEGMENTATION USING A REFINED FULLY CONVOLUTIONAL NETWORK

Traditional segmentation method has the problem that background noise disturbs to the accuracy of segmentation. To improve the segmentation accuracy, an improved fully convolutional network (FCN) for plant leaf segmentation is proposed in this paper. In order to realize the rapid and accurate segmentation of plant leaves, firstly, the original skip connection structure was improved by reducing the number of feature maps in convolution layer C6. Secondly, features of the shallower layers were further integrated. Thirdly, the direct-connected structure (Direct-FCNs) was adopted by removing some layers. Moreover, the feature maps reduced by the VGG16 model were up-sampled to the original image size by de-convolution. Therefore, several improved models were obtained. Finally, much images were divided into training set and test set. The proposed method is compared with other traditional methods. It can be observed from all the experimental results that the refined model has higher segmentation accuracy, less parameters and stronger robustness, which is able to be used for the automatic segmentation of plant leaves.

PERFORMANCE COMPARISON OF UNSUPERVISED SEGMENTATION ALGORITHMS ON RICE, GROUNDNUT, AND APPLE PLANT LEAF IMAGES

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 semantic segmentation for automatic recognition and analysis of plant leaf disease detection is extremely demanded in the area of precision agriculture. This breakthrough is expected to lead towards the demand for an accurate and reliable technique for plant leaf segmentation. We propose a hybrid variant that incorporates Graph Cut (GC) and Multi-Level Otsu (MOTSU) in this paper. We compare the segmentation performance implemented on rice, groundnut, and apple plant leaf images for various unsupervised segmentation algorithms. Boundary Displacement error (BDe), Global Consistency error (GCe), Variation of Information (VoI), and Probability Rand index (PRi), are the index metrics used to evaluate the performance of the proposed model. By comparing the outcomes of the simulation, it demonstrates that our proposed technique, Graph Cut based Multi-level Otsu (GCMO), provides better segmentation results as compared to other existing unsupervised algorithms.

Plant Leaf Segmentation and Phenotypic Analysis Based on Fully Convolutional Neural Network

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 images can achieve high-throughput image processing. This article proposes an improved U-Net segmentation network, based on small sample data enhancement, and reconstructs the U-Net model by optimizing the model framework, activation function and loss function. It is used to realize automatic segmentation of plant leaf images and extract relevant feature parameters. Experimental results show that the improved model can provide reliable segmentation results under different leaf sizes, different lighting conditions, different backgrounds, and different plant leaves. The pixel-by-pixel segmentation accuracy reaches 0.94. Compared with traditional methods, this network achieves robust and high-throughput image segmentation. This method is expected to provide key technical support and practical tools for top-view image processing, Unmanned Aerial Vehicle phenotype extraction, and phenotype field platforms. Keywords: Deep learning, Full convolution neural network, Image segmentation, Phenotype analysis, U-Net.

A novel approach for unsupervised image segmentation fusion of plant leaves based on G-mutual information

Plant leaf segmentation has a very important role in most plant identification methods. Tree leaves segmentation in images with complex background is very difficult when there is no prior information about the leaves and backgrounds. In practice, the parameters of unsupervised image segmentation algorithms must be set for each image to get the best results. In this paper, to overcome this problem, fusion of the results of five leaf segmentation algorithms (fuzzy c-means, SOM and k-means in various color spaces or different parameters) is applied. To fuse the results of these segmentations, new equations for mutual information (g-mutual information equations) based on the g-calculus are introduced to find the best consensus segmentation. The results of the mentioned primary clustering algorithms are considered as a new feature vector for each pixel. To reduce the time complexity, a fast method is employed using truth table containing different feature vectors. To evaluate this new approach, a leaf image database with natural scenes, taken from Pl@ntLeaves database, is generated to have different positions and orientations. In addition, a widely used database is used to compare the proposed method with other methods. The experimental results presented in this paper show that the use of g-calculus in fusion of image segmentations improves the evaluation parameters.

Exemplar-Based Recursive Instance Segmentation With Application to Plant Image Analysis.

Instance segmentation is a challenging computer vision problem which lies at the intersection of object detection and semantic segmentation. Motivated by plant image analysis in the context of plant phenotyping, a recently emerging application field of computer vision, this paper presents the Exemplar-Based Recursive Instance Segmentation (ERIS) framework. A three-layer probabilistic model is firstly introduced to jointly represent hypotheses, voting elements, instance labels and their connections. Afterwards, a recursive optimization algorithm is developed to infer the maximum a posteriori (MAP) solution, which handles one instance at a time by alternating among the three steps of detection, segmentation and update. The proposed ERIS framework departs from previous works mainly in two respects. First, it is exemplar-based and model-free, which can achieve instance-level segmentation of a specific object class given only a handful of (typically less than 10) annotated exemplars. Such a merit enables its use in case that no massive manually-labeled data is available for training strong classification models, as required by most existing methods. Second, instead of attempting to infer the solution in a single shot, which suffers from extremely high computational complexity, our recursive optimization strategy allows for reasonably efficient MAP-inference in full hypothesis space. The ERIS framework is substantialized for the specific application of plant leaf segmentation in this work. Experiments are conducted on public benchmarks to demonstrate the superiority of our method in both effectiveness and efficiency in comparison with the state-of-the-art.

Automated segmentation of soybean plants from 3D point cloud using machine learning

Image-based plant phenotyping has become a promising method for high-throughput measurement of plant traits in breeding programs. Plant geometric features that are essential for understanding plant growth can be obtained from the point cloud built using three-dimensional (3D) reconstruction of plant imagery data. A key task in the data processing pipeline is the automated and accurate segmentation of individual plants. Machine learning is a promising approach due to its strong ability in the extraction of details from images and has been successfully applied in plant leaf segmentation from two-dimensional (2D) images. The aim of this paper was to evaluate the performance of three machine learning methods, i.e. boosting, Support Vector Machine (SVM) and K-means clustering, in the segmentation of non-overlapped and overlapped soybean plants at early growth stages using 3D point cloud. Images of 75 soybean plants at two growth stages in a greenhouse were collected using an image-based high-throughput phenotyping platform and were used to develop 3D point cloud using the Structure from Motion (SfM) method. Plant features including position (coordinate x, y, and z), and color (Red, Green, Blue, hue, saturation and Triangular Greenness Index) were used for background removal and the separation of non-overlapped plants. A Histogram of Oriented Gradient (HOG) descriptor was used for the separation of overlapped plants. The percentage of mismatched points between manual and automated segmentation was calculated and results showed that K-means clustering had the least mean error rates (0.36% and 0.20%) for the background removal and the non-overlapped plant separation. The least mean error rate for the separation of overlapped plants was 2.57% using SVM with labeled HOG descriptor. The developed image segmentation pipeline was evaluated in a case study where 69 plants at different growth stages were continuously monitored. Results showed that it took three minutes on average for completing all procedures in the pipeline and the extracted features (i.e. height and shooting area) were able to quantify the plant growth.

A review for the automatic methods of plant’s leaf image segmentation

Various plant species had disappeared and many are on the verge of disappearing. Our economy highly depends on agricultural productivity. Plants play an important role in the agriculture field. Plant's leaf segmentation has become an important task in the agricultural fields. In the leaf segmentation process, a few generalised steps are followed such as leaf image acquisition, leaf image pre-processing, feature extraction and leaf image segmentation. It is important to identify and differentiate various features from a noisy background. Feature extraction is the intermediary step to identify the features in leaves such as leaf area, leaf colour, leaf texture, etc. There are various techniques used for both feature extraction as well as segmentation of leaves like edge detection technique, K-means clustering techniques, artificial neural network-based techniques, etc. This paper presents a review of some methods used for the segmentation of leaf based on the available literature.

In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation.

In this paper, we present a challenging task of 3D segmentation of individual plant leaves from occlusions in the complicated natural scene. Depth data of plant leaves is introduced to improve the robustness of plant leaf segmentation. The low cost RGB-D camera is utilized to capture depth and color image in fields. Mean shift clustering is applied to segment plant leaves in depth image. Plant leaves are extracted from the natural background by examining vegetation of the candidate segments produced by mean shift. Subsequently, individual leaves are segmented from occlusions by active contour models. Automatic initialization of the active contour models is implemented by calculating the center of divergence from the gradient vector field of depth image. The proposed segmentation scheme is tested through experiments under greenhouse conditions. The overall segmentation rate is 87.97% while segmentation rates for single and occluded leaves are 92.10% and 86.67%, respectively. Approximately half of the experimental results show segmentation rates of individual leaves higher than 90%. Nevertheless, the proposed method is able to segment individual leaves from heavy occlusions.

In vitro shoot organogenesis from Eucalyptussp. leaf explants

Background In vitro organogenesis is one of the key techniques associated with genetic transformation, as it determines the successful regeneration of transgenic plants after co-cultivation with bacteria. Therefore, the development of efficient regeneration protocols is the most critical step in developing genetic transformation. Protocols have been developed for several species of eucalyptus. In most of the studies cotyledon, hypocotyl and leaf segments of plants cultivated in vitro are used as explants [1]. Several eucalyptus functional genomics projects have used Populus species as a model for it counts with well established regeneration and transformation protocols. However, the use of Eucalyptus clones as model plants could be more adequate, if clones with high regeneration rates as those obtained for Populus could be found. The aim of this study was to evaluate several variables in the in vitro organogenesis from leaves of an Eucalyptus sp. clone maintained in vitro at Embrapa Forestry.

Influence of exogenously supplied KCl on water binding mode of durum wheat leaf segments

Durum wheat leaf segments (Triticum durum Desf.) were incubated in solutions of KC1 to test the effect of selective accumulation of ions, on tissues affinity for water. This was estimated trough an indirect estimate of the average enthalpy of water sorption. Leaf segments were collected from well watered plants grown in a controlled environment and were incubated in different KC1 solutions. As increasing concentrations of KC1 were included in the medium accumulation of K + and Cl - ions and changes in the water-binding mode of the leaf segments were observed. Large increases in tissue affinity for water appeared when KC1 concentrations in the medium were between 20-30mM and 50mM. These findings were repeatable in leaf segments of plants that differed in both growth stage and genotype. Relationships between tissue affinity for water and tissue K + and Cl - content were examined. Tissue affinity for water did not change until the concentration of K + in the leaf tissues reached approximately 50-55 mg/g dry weight, where large increase in strength of water binding was observed. Above this threshold tissue affinity for water was low even if K + content increased.

Uptake, translocation and activity of paraquat on Mikania micrantha H.B.K. grown in different light conditions

Abstract A glasshouse study was conducted to determine the effect of shade on the uptake, translocation and activity of paraquat on Mikania micrantha H.B.K. The uptake, translocation and activity of paraquat were significantly enhanced at low light intensity. Placement of plants grown initially under 75% shade and transferred to 0% shade resulted in significantly higher uptake of paraquat than those initially grown under 0% and 50% shade. The uptake and activity of paraquat were also significantly increased when treated plants were placed in darkness. The rate of efflux of paraquat from leaf segments of plants grown in high light intensity was considerably slower than from plants grown in low light intensity.