Leaf Counting Research Articles

Sorghum segmentation and leaf counting using in silico trained deep neural model

AbstractThis paper introduces a novel deep neural model for segmenting and tracking the number of leaves in sorghum plants in phenotyping facilities. Our algorithm inputs a sequence of images of a sorghum plant and outputs the segmented images and the number of leaves. The key novelty of our approach is in training the deep neural model. Manual annotations are tedious, and we have developed a procedural three‐dimensional (3D) sorghum model that provides detailed geometry and texture to generate photorealistic 3D models. The overall shape of the sorghum leaf geometry is determined by its skeleton, and it is detailed by a procedural model that varies its curvature, width, length, and overall shape. The color is determined by using a Monte Carlo path tracer. We mimic the illumination of the phenotyping facility and use reflectance and transmittance on sorghum surfaces to determine the color of the leaves. The 3D procedural model allows us to generate photorealistic and segmented images that we use to train a deep neural model. Our segmentation provides a mean intersection over union score of 0.51, resulting in leaf counting accuracy within the 90% confidence interval for the human counts.

Lightweight Corn Leaf Detection and Counting Using Improved YOLOv8.

The number of maize leaves is an important indicator for assessing plant growth and regulating population structure. However, the traditional leaf counting method mainly relies on manual work, which is both time-consuming and straining, while the existing image processing methods have low accuracy and poor adaptability, making it difficult to meet the standards for practical application. To accurately detect the growth status of maize, an improved lightweight YOLOv8 maize leaf detection and counting method was proposed in this study. Firstly, the backbone of the YOLOv8 network is replaced using the StarNet network and the convolution and attention fusion module (CAFM) is introduced, which combines the local convolution and global attention mechanisms to enhance the ability of feature representation and fusion of information from different channels. Secondly, in the neck network part, the StarBlock module is used to improve the C2f module to capture more complex features while preserving the original feature information through jump connections to improve training stability and performance. Finally, a lightweight shared convolutional detection head (LSCD) is used to reduce repetitive computations and improve computational efficiency. The experimental results show that the precision, recall, and mAP50 of the improved model are 97.9%, 95.5%, and 97.5%, and the numbers of model parameters and model size are 1.8 M and 3.8 MB, which are reduced by 40.86% and 39.68% compared to YOLOv8. This study shows that the model improves the accuracy of maize leaf detection, assists breeders in making scientific decisions, provides a reference for the deployment and application of maize leaf number mobile end detection devices, and provides technical support for the high-quality assessment of maize growth.

A CNN-based model to count the leaves of rosette plants (LC-Net)

Plant image analysis is a significant tool for plant phenotyping. Image analysis has been used to assess plant trails, forecast plant growth, and offer geographical information about images. The area segmentation and counting of the leaf is a major component of plant phenotyping, which can be used to measure the growth of the plant. Therefore, this paper developed a convolutional neural network-based leaf counting model called LC-Net. The original plant image and segmented leaf parts are fed as input because the segmented leaf part provides additional information to the proposed LC-Net. The well-known SegNet model has been utilised to obtain segmented leaf parts because it outperforms four other popular Convolutional Neural Network (CNN) models, namely DeepLab V3+, Fast FCN with Pyramid Scene Parsing (PSP), U-Net, and Refine Net. The proposed LC-Net is compared to the other recent CNN-based leaf counting models over the combined Computer Vision Problems in Plant Phenotyping (CVPPP) and KOMATSUNA datasets. The subjective and numerical evaluations of the experimental results demonstrate the superiority of the LC-Net to other tested models.

Prediction of Duration of Dry Bamboo Leaf Counting Using Fuzzy Logic

Dry bamboo leaves are selected as a planting medium for ornamental plants to enhance nutrient content and improve soil drainage. Before being processed into fertilizer, dry bamboo leaves need to be shredded first. The leaf counting process for planting ornamental plants in villages still relies on manual methods such as using knives and scissors, which can be time-consuming and less effective. In response to this issue, a device for shredding dry bamboo leaves has been developed to improve efficiency and facilitate the leaf counting process. The innovation created is a household-scale dry bamboo leaf shredder that is more affordable and easy to mobilize due to its compact dimensions and lighter weight. The device is also easy to operate, as the shredding occurs when it is closed, and the process stops when it is opened, ensuring safe use for various users. The manual control of bamboo leaf weight and counting duration results in inconsistent shredding outcomes. Therefore, development has been carried out on the dry bamboo leaf shredder with artificial intelligence capabilities, specifically using fuzzy logic to automate the counting duration based on the size and weight of the bamboo leaves being inputted.

Maize Seedling Leave Counting Based on Semi-Supervised Learning and UAV RGB Images

The number of leaves in maize seedlings is an essential indicator of their growth rate and status. However, manual counting of seedlings is inefficient and limits the scope of the investigation. Deep learning has shown potential for quickly identifying seedlings, but it requires larger, labeled datasets. To address these challenges, we proposed a method for counting maize leaves from seedlings in fields using a combination of semi-supervised learning, deep learning, and UAV digital imagery. Our approach leveraged semi-supervised learning and novel methods for detecting and counting maize seedling leaves accurately and efficiently. Specifically, we used a small amount of labeled data to train the SOLOv2 model based on the semi-supervised learning framework Noisy Student. This model can segment complete maize seedlings from UAV digital imagery and generate foreground images of maize seedlings with background removal. We then trained the YOLOv5x model based on Noisy Student with a small amount of labeled data to detect and count maize leaves. We divided our dataset of 1005 images into 904 training images and 101 testing images, and randomly divided the 904 training images into four sets of labeled and unlabeled data with proportions of 4:6, 3:7, 2:8, and 1:9, respectively. The results indicated that the SOLOv2 Resnet101 outperformed the SOLOv2 Resnet50 in terms of segmentation performance. Moreover, when the labeled proportion was 30%, the student model SOLOv2 achieved a similar segmentation performance to the fully supervised model with a mean average precision (mAP) of 93.6%. When the labeled proportion was 40%, the student model YOLOv5x demonstrated comparable leaf counting performance to the fully supervised model. The model achieved an average precision of 89.6% and 57.4% for fully unfolded leaves and newly appearing leaves, respectively, with counting accuracy rates of 69.4% and 72.9%. These results demonstrated that our proposed method based on semi-supervised learning and UAV imagery can advance research on crop leaf counting in fields and reduce the workload of data annotation.

Leaf counting in the presence of occlusion in Arabidopsis thaliana plant using convolutional neural networks

Leaf counting in the presence of occlusion in Arabidopsis thaliana plant using convolutional neural networks

MESIN PENCACAH PELEPAH DAN DAUN KELAPA SAWIT UNTUK PAKAN KAMBING DI DESA PUDING BESAR

In the process of raising goats, one of the factors that affect the process of goat growth and development is the availability of feed in the surrounding environment. In an effort to meet this availability, it is necessary to have a food source that is always available and usually comes from plants that exist in nature, such as grass or leaves. The availability of animal feed is not immune from the influence of weather conditions and the level of availability of weeds around it which are needed in large quantities. To deal with this, some farmers use alternative feed sources, one of which has been used is palm fronds and leaves and other organic waste. With the size of the palm frond which is quite large, it is necessary to chop it up so that it can be consumed directly by goats (Latifa S, Anto A, David S, Jeni W, Alexander Y). Of course, manual counting can be done, but it is not very effective and requires a lot of time and energy. Based on these problems, it is necessary to mechanization of the midrib and leaf counting process in order to facilitate the process. The purpose of science and technology for the community in this goat farmer group in general is to improve the quality and capacity of livestock production. In addition, by helping the availability of goat feed through the use of alternative feed sources other than the sources that have been available so far and applying appropriate technology for the process of chopping palm fronds and other organic waste. This machine is capable of chopping palm fronds and leaves with a maximum size of 2 cm. Based on the results of interviews with farmers, this size is a size that can be chewed or processed by goats when eating. Then, through this IbM activity, it is expected to be able to establish cooperation between partners and proposers from educational institutions. Partners can develop their business because the need for animal feed is more fulfilled through animal feed chopper machines, while institutions can directly apply science and technology and apply their knowledge into appropriate technology and can introduce Polmanbabel educational institutions more broadly to the community.

Multi-view triangulation without correspondences

We introduce a novel method to solve the joint problem of correspondence and triangulation of points from multiple calibrated perspective views and show its application to counting the number of leaves present on plants photographed from multiple angles in phenotyping facilities. Assuming there is a set of n calibrated views of an object, the input to our algorithm is a set of 2D points (e.g., leaf tips, fruits, flowers) detected in each of the n views, and the output is a set of 3D points corresponding to the 2D points when re-projected on views. Our approach is robust to noise and occlusion. In particular, it is not required for all points to be visible from one of the views, as our algorithm can infer that a point is occluded by reasoning on the 3D geometry of the scene. Our algorithm is suitable for many points (approx. thousands) reconstructed from a reduced set of views (up to 15). For example, our implementation finds the correspondence of 20 points captured by six cameras in about one second on consumer hardware. We evaluate the performance on synthetic data as well as real examples. We show that the accuracy of leaf counting from multi-view images is drastically improved by our algorithm.

Leaf-Counting in Monocot Plants Using Deep Regression Models.

Leaf numbers are vital in estimating the yield of crops. Traditional manual leaf-counting is tedious, costly, and an enormous job. Recent convolutional neural network-based approaches achieve promising results for rosette plants. However, there is a lack of effective solutions to tackle leaf counting for monocot plants, such as sorghum and maize. The existing approaches often require substantial training datasets and annotations, thus incurring significant overheads for labeling. Moreover, these approaches can easily fail when leaf structures are occluded in images. To address these issues, we present a new deep neural network-based method that does not require any effort to label leaf structures explicitly and achieves superior performance even with severe leaf occlusions in images. Our method extracts leaf skeletons to gain more topological information and applies augmentation to enhance structural variety in the original images. Then, we feed the combination of original images, derived skeletons, and augmentations into a regression model, transferred from Inception-Resnet-V2, for leaf-counting. We find that leaf tips are important in our regression model through an input modification method and a Grad-CAM method. The superiority of the proposed method is validated via comparison with the existing approaches conducted on a similar dataset. The results show that our method does not only improve the accuracy of leaf-counting, with overlaps and occlusions, but also lower the training cost, with fewer annotations compared to the previous state-of-the-art approaches.The robustness of the proposed method against the noise effect is also verified by removing the environmental noises during the image preprocessing and reducing the effect of the noises introduced by skeletonization, with satisfactory outcomes.

Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant Phenotyping.

The rise of self-supervised learning (SSL) methods in recent years presents an opportunity to leverage unlabeled and domain-specific datasets generated by image-based plant phenotyping platforms to accelerate plant breeding programs. Despite the surge of research on SSL, there has been a scarcity of research exploring the applications of SSL to image-based plant phenotyping tasks, particularly detection and counting tasks. We address this gap by benchmarking the performance of 2 SSL methods-momentum contrast (MoCo) v2 and dense contrastive learning (DenseCL)-against the conventional supervised learning method when transferring learned representations to 4 downstream (target) image-based plant phenotyping tasks: wheat head detection, plant instance detection, wheat spikelet counting, and leaf counting. We studied the effects of the domain of the pretraining (source) dataset on the downstream performance and the influence of redundancy in the pretraining dataset on the quality of learned representations. We also analyzed the similarity of the internal representations learned via the different pretraining methods. We find that supervised pretraining generally outperforms self-supervised pretraining and show that MoCo v2 and DenseCL learn different high-level representations compared to the supervised method. We also find that using a diverse source dataset in the same domain as or a similar domain to the target dataset maximizes performance in the downstream task. Finally, our results show that SSL methods may be more sensitive to redundancy in the pretraining dataset than the supervised pretraining method. We hope that this benchmark/evaluation study will guide practitioners in developing better SSL methods for image-based plant phenotyping.

Detection and Counting of Maize Leaves Based on Two-Stage Deep Learning with UAV-Based RGB Image

Leaf age is an important trait in the process of maize (Zea mays L.) growth. It is significant to estimate the seed activity and yield of maize by counting leaves. Detection and counting of the maize leaves in the field are very difficult due to the complexity of the field scenes and the cross-covering of adjacent seedling leaves. A method was proposed in this study for detecting and counting maize leaves based on deep learning with RGB images collected by unmanned aerial vehicles (UAVs). The Mask R-CNN was used to separate the complete maize seedlings from the complex background to reduce the impact of weeds on leaf counting. We proposed a new loss function SmoothLR for Mask R-CNN to improve the segmentation performance of the model. Then, YOLOv5 was used to detect and count the individual leaves of maize seedlings after segmentation. The 1005 field seedlings images were randomly divided into the training, validation, and test set with the ratio of 7:2:1. The results showed that the segmentation performance of Mask R-CNN with Resnet50 and SmoothLR was better than that with LI Loss. The average precision of the bounding box (Bbox) and mask (Mask) was 96.9% and 95.2%, respectively. The inference time of single image detection and segmentation was 0.05 s and 0.07 s, respectively. YOLOv5 performed better in leaf detection compared with Faster R-CNN and SSD. YOLOv5x with the largest parameter had the best detection performance. The detection precision of fully unfolded leaves and newly appeared leaves was 92.0% and 68.8%, and the recall rates were 84.4% and 50.0%, respectively. The average precision (AP) was 89.6% and 54.0%, respectively. The rates of counting accuracy for newly appeared leaves and fully unfolded leaves were 75.3% and 72.9%, respectively. The experimental results showed the possibility of current research on exploring leaf counting for field-grown crops based on UAV images.

Unsupervised adversarial domain adaptation leaf counting with Bayesian loss density estimation

Unsupervised adversarial domain adaptation leaf counting with Bayesian loss density estimation

A Segmentation-Guided Deep Learning Framework for Leaf Counting.

Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. The experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes.

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.

Toward automatic plant phenotyping: starting from leaf counting

Toward automatic plant phenotyping: starting from leaf counting

Leaf Counting: Fusing Network Components for Improved Accuracy.

Leaf counting in potted plants is an important building block for estimating their health status and growth rate and has obtained increasing attention from the visual phenotyping community in recent years. Two novel deep learning approaches for visual leaf counting tasks are proposed, evaluated, and compared in this study. The first method performs counting via direct regression but using multiple image representation resolutions to attend leaves of multiple scales. The leaf count from multiple resolutions is fused using a novel technique to get the final count. The second method is detection with a regression model that counts the leaves after locating leaf center points and aggregating them. The algorithms are evaluated on the Leaf Counting Challenge (LCC) dataset of the Computer Vision Problems in Plant Phenotyping (CVPPP) conference 2017, and a new larger dataset of banana leaves. Experimental results show that both methods outperform previous CVPPP LCC challenge winners, based on the challenge evaluation metrics, and place this study as the state of the art in leaf counting. The detection with regression method is found to be preferable for larger datasets when the center-dot annotation is available, and it also enables leaf center localization with a 0.94 average precision. When such annotations are not available, the multiple scale regression model is a good option.

Automation of leaf counting in maize and sorghum using deep learning

AbstractLeaf number and leaf emergence rate are phenotypes of interest to plant breeders, plant geneticists, and crop modelers. Counting the extant leaves of an individual plant is straightforward even for an untrained individual, but manually tracking changes in leaf numbers for hundreds of individuals across multiple time points is logistically challenging. This study generated a dataset including over 150,000 maize and sorghum images for leaf counting projects. A subset of 17,783 images also includes annotations of the positions of individual leaf tips. With these annotated images, we evaluate two deep learning‐based approaches for automated leaf counting: the first based on counting‐by‐regression from whole image analysis and a second based on counting‐by‐detection. Both approaches can achieve root of mean square error (RMSE) smaller than one leaf, only moderately inferior to the RMSE between human annotators of between 0.57 and 0.73 leaves. The counting‐by‐regression approach based on convolutional neural networks (CNNs) exhibited lower accuracy and increased bias for plants with extreme leaf numbers which are underrepresented in this dataset. The counting‐by‐detection approach based on Faster R‐CNNs (region based convolutional neural networks) object detection models achieve near human performance for plants where all leaf tips are visible. The annotated image data and model performance metrics generated as part of this study provide large scale resources for the comparison and improvement of algorithms for leaf counting from image data in grain crops.

Real-Time Plant Leaf Counting Using Deep Object Detection Networks.

The use of deep neural networks (DNNs) in plant phenotyping has recently received considerable attention. By using DNNs, valuable insights into plant traits can be readily achieved. While these networks have made considerable advances in plant phenotyping, the results are processed too slowly to allow for real-time decision-making. Therefore, being able to perform plant phenotyping computations in real-time has become a critical part of precision agriculture and agricultural informatics. In this work, we utilize state-of-the-art object detection networks to accurately detect, count, and localize plant leaves in real-time. Our work includes the creation of an annotated dataset of Arabidopsis plants captured using Cannon Rebel XS camera. These images and annotations have been complied and made publicly available. This dataset is then fed into a Tiny-YOLOv3 network for training. The Tiny-YOLOv3 network is then able to converge and accurately perform real-time localization and counting of the leaves. We also create a simple robotics platform based on an Android phone and iRobot create2 to demonstrate the real-time capabilities of the network in the greenhouse. Additionally, a performance comparison is conducted between Tiny-YOLOv3 and Faster R-CNN. Unlike Tiny-YOLOv3, which is a single network that does localization and identification in a single pass, the Faster R-CNN network requires two steps to do localization and identification. While with Tiny-YOLOv3, inference time, F1 Score, and false positive rate (FPR) are improved compared to Faster R-CNN, other measures such as difference in count (DiC) and AP are worsened. Specifically, for our implementation of Tiny-YOLOv3, the inference time is under 0.01 s, the F1 Score is over 0.94, and the FPR is around 24%. Last, transfer learning using Tiny-YOLOv3 to detect larger leaves on a model trained only on smaller leaves is implemented. The main contributions of the paper are in creating dataset (shared with the research community), as well as the trained Tiny-YOLOv3 network for leaf localization and counting.

Leaf Counting with Multi-Scale Convolutional Neural Network Features and Fisher Vector Coding

The number of leaves in maize plant is one of the key traits describing its growth conditions. It is directly related to plant development and leaf counts also give insight into changing plant development stages. Compared with the traditional solutions which need excessive human interventions, the methods of computer vision and machine learning are more efficient. However, leaf counting with computer vision remains a challenging problem. More and more researchers are trying to improve accuracy. To this end, an automated, deep learning based approach for counting leaves in maize plants is developed in this paper. A Convolution Neural Network(CNN) is used to extract leaf features. The CNN model in this paper is inspired by Google Inception Net V3, which using multi-scale convolution kernels in one convolution layer. To compress feature maps generated from some middle layers in CNN, the Fisher Vector (FV) is used to reduce redundant information. Finally, these encoded feature maps are used to regress the leaf numbers by using Random Forests. To boost the related research, a relatively single maize image dataset (Different growth stage with 2845 samples, which 80% for train and 20% for test) is constructed by our team. The proposed algorithm in single maize data set achieves Mean Square Error (MSE) of 0.32.

Image based leaf segmentation and counting in rosette plants

This paper proposes an efficient method to extract the leaf region and count the number of leaves in digital plant images. The plant image analysis plays a significant role in viable and productive agriculture. It is used to record the plant growth, plant yield, chlorophyll fluorescence, plant width and tallness, leaf area, etc. frequently and accurately. Plant growth is a major character to be analyzed among these plant characters and it directly depends on the number of leaves in the plants. In this paper, a new method is presented for leaf region extraction from plant images and counting the number of leaves. The proposed method has three steps. The first step involves a new statistical based technique for image enhancement. The second step involves in the extraction of leaf region in plant image using a graph based method. The third step involves in counting the number of leaves in the plant image by applying Circular Hough Transform (CHT). The proposed work has been experimented on benchmark datasets of Leaf Segmentation Challenge (LSC). The proposed method achieves the segmentation accuracy of 95.4% and it also achieves the counting accuracy of −0.7 (DiC) and 2.3 (|DiC|) for datasets (A1, A2 and A3), which are better than the state-of-the-art methods.