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The multiclass confusion matrix of the different classification results using the test data. The method achieved good results in the classification of each category
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Background:
Wheat yield is influenced by the number of ears per unit area, and manual counting has traditionally been used to estimate wheat yield. To realize rapid and accurate wheat ear counting, K-means clustering was used for the automatic segmentation of wheat ear images captured by hand-held devices. The segmented data set was constructed by...
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Context 1
... assess the classification results, after 8000 epochs of training, we adopted indices of macro F1-score and micro F1-score calculated on a multiclass confusion matrix. The classification results obtained by the methods are shown in Fig. 7 and Table 3. Figure 7 lists the confusion matrix in detail, which calculates the statistics of the ...
Context 2
... assess the classification results, after 8000 epochs of training, we adopted indices of macro F1-score and micro F1-score calculated on a multiclass confusion matrix. The classification results obtained by the methods are shown in Fig. 7 and Table 3. Figure 7 lists the confusion matrix in detail, which calculates the statistics of the classified ...
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Citations
... The automatic detection of wheat ear in the field has attracted the attention of many scholars [5][6][7], mainly focusing on two categories of methods, machine learning and deep learning, and has made certain research progress. Traditional machine learning methods first extract features such as ear shape, texture, and color from acquired wheat RGB images and then use classifier models to achieve ear object detection. ...
Wheat ear counting is crucial for calculating wheat phenotypic parameters and scientifically managing fields, which is essential for estimating wheat field yield. In wheat fields, detecting wheat ears can be challenging due to factors such as changes in illumination, wheat ear growth posture, and the appearance color of wheat ears. To improve the accuracy and efficiency of wheat ear detection and meet the demands of intelligent yield estimation, this study proposes an efficient model, Generalized Focal Loss WheatNet (GFLWheatNet), for wheat ear detection. This model precisely counts small, dense, and overlapping wheat ears. Firstly, in the feature extraction stage, we discarded the C4 feature layer of the ResNet50 and added the Convolutional block attention module (CBAM) to this location. This step maintains strong feature extraction capabilities while reducing redundant feature information. Secondly, in the reinforcement layer, we designed a skip connection module to replace the multi-scale feature fusion network, expanding the receptive field to adapt to various scales of wheat ears. Thirdly, leveraging the concept of distribution-guided localization, we constructed a detection head network to address the challenge of low accuracy in detecting dense and overlapping targets. Validation on the publicly available Global Wheat Head Detection dataset (GWHD-2021) demonstrates that GFLWheatNet achieves detection accuracies of 43.3% and 93.7% in terms of mean Average Precision (mAP) and AP50 (Intersection over Union (IOU) = 0.5), respectively. Compared to other models, it exhibits strong performance in terms of detection accuracy and efficiency. This model can serve as a reference for intelligent wheat ear counting during wheat yield estimation and provide theoretical insights for the detection of ears in other grain crops.
... All of the above methods use data labeling, but it is labor intensive, so the researchers again proposed unsupervised segmentation of the wheat ear. For example (Xu et al., 2020) used kmean clustering technique to automatically segment the images of wheat ears counts collected by handheld devices for fast and accurate wheat ears counts, and the recognition rate of wheat reached up to 98.5%. Machine learning is able to learn target features from given data to achieve better recognition, so the accuracy of target feature selection determines the effectiveness of this type of method, but it needs to be determined by the researcher to determine the target features, which is subjective. ...
Introduction
Field wheat ear counting is an important step in wheat yield estimation, and how to solve the problem of rapid and effective wheat ear counting in a field environment to ensure the stability of food supply and provide more reliable data support for agricultural management and policy making is a key concern in the current agricultural field.
Methods
There are still some bottlenecks and challenges in solving the dense wheat counting problem with the currently available methods. To address these issues, we propose a new method based on the YOLACT framework that aims to improve the accuracy and efficiency of dense wheat counting. Replacing the pooling layer in the CBAM module with a GeM pooling layer, and then introducing the density map into the FPN, these improvements together make our method better able to cope with the challenges in dense scenarios.
Results
Experiments show our model improves wheat ear counting performance in complex backgrounds. The improved attention mechanism reduces the RMSE from 1.75 to 1.57. Based on the improved CBAM, the R2 increases from 0.9615 to 0.9798 through pixel-level density estimation, the density map mechanism accurately discerns overlapping count targets, which can provide more granular information.
Discussion
The findings demonstrate the practical potential of our framework for intelligent agriculture applications.
... Traditional machine learning methods depend on manual feature engineering and classification for spike detection [6][7][8]. However, the generalization ability of these methods drops significantly when the application domain or environment changes [9,10]. With the rapid development of artificial intelligence technology, deep learning-based object detection methods have demonstrated excellent performance in the agricultural field, introducing innovative possibilities for detecting distortion, the incorporation of the SIOU (SCYLLA intersection over union) loss function may lead to favorable outcomes. ...
The rice spike, a crucial part of rice plants, plays a vital role in yield estimation, pest detection, and growth stage management in rice cultivation. When using drones to capture photos of rice fields, the high shooting angle and wide coverage area can cause rice spikes to appear small in the captured images and can cause angular distortion of objects at the edges of images, resulting in significant occlusions and dense arrangements of rice spikes. These factors are unique challenges during drone image acquisition that may affect the accuracy of rice spike detection. This study proposes a rice spike detection method that combines deep learning algorithms with drone perspectives. Initially, based on an enhanced version of YOLOv5, the EMA (efficient multiscale attention) attention mechanism is introduced, a novel neck network structure is designed, and SIoU (SCYLLA intersection over union) is integrated. Experimental results demonstrate that RICE-YOLO achieves a mAP@0.5 of 94.8% and a recall of 87.6% on the rice spike dataset. During different growth stages, it attains an AP@0.5 of 96.1% and a recall rate of 93.1% during the heading stage, and a AP@0.5 of 86.2% with a recall rate of 82.6% during the filling stage. Overall, the results indicate that the proposed method enables real-time, efficient, and accurate detection and counting of rice spikes in field environments, offering a theoretical foundation and technical support for real-time and efficient spike detection in the management of rice growth processes.
... Consequently, numerous researchers have endeavored to apply general object detection algorithms to the field of agricultural object detection. Xu et al. [9] employed the K-means clustering algorithm to automatically segment wheat head images and extract wheat head contour features, thereby significantly enhancing the efficiency and accuracy of wheat head counting. Wang et al. [10] utilized a multilevel neural network (SSRNET) for wheat head image segmentation, achieving rapid estimation of wheat head quantities under field conditions. ...
Wheat breeding heavily relies on the observation of various traits during the wheat growth process. Among all traits, wheat head density stands out as a particularly crucial characteristic. Despite the realization of high-throughput phenotypic data collection for wheat, the development of efficient and robust models for extracting traits from raw data remains a significant challenge. Numerous fully supervised target detection algorithms have been employed to address the wheat head detection problem. However, constrained by the exorbitant cost of dataset creation, especially the manual annotation cost, fully supervised target detection algorithms struggle to unleash their full potential. Semi-supervised training methods can leverage unlabeled data to enhance model performance, addressing the issue of insufficient labeled data. This paper introduces a one-stage anchor-based semi-supervised wheat head detector, named “Wheat Teacher”, which combines two semi-supervised methods, pseudo-labeling, and consistency regularization. Furthermore, two novel dynamic threshold components, Pseudo-label Dynamic Allocator and Loss Dynamic Threshold, are designed specifically for wheat head detection scenarios to allocate pseudo-labels and filter losses. We conducted detailed experiments on the largest wheat head public dataset, GWHD2021. Compared with various types of detectors, Wheat Teacher achieved a mAP0.5 of 92.8% with only 20% labeled data. This result surpassed the test outcomes of two fully supervised object detection models trained with 100% labeled data, and the difference with the other two fully supervised models trained with 100% labeled data was within 1%. Moreover, Wheat Teacher exhibits improvements of 2.1%, 3.6%, 5.1%, 37.7%, and 25.8% in mAP0.5 under different labeled data usage ratios of 20%, 10%, 5%, 2%, and 1%, respectively, validating the effectiveness of our semi-supervised approach. These experiments demonstrate the significant potential of Wheat Teacher in wheat head detection.
... In the soybean production process, the seedling stage's emergence rate is considered an essential decision indicator for subsequent production management and a key reference for yield prediction. Traditionally, the evaluation of soybean emergence rate is often done through a combination of manual counting and sampling methods. This method has proven to be labor-intensive and susceptible to inaccuracies, stemming from factors such as the density of the plants, limitations in human visual perception, the representativeness of the samples taken, and the methodologies employed in sampling [1][2][3]. Additionally, it is challenging to meet the needs of continuous spatiotemporal monitoring of large-scale fields with these methods. Therefore, it is necessary to find a rapid and highly accurate detection method for the emergence rate of soybean seedlings that is suitable for large-scale areas. ...
During the growth stage of soybean seedlings, it is crucial to quickly and precisely identify them for emergence rate assessment and field management. Traditional manual counting methods have some limitations in scenarios with large-scale and high-efficiency requirements, such as being time-consuming, labor-intensive, and prone to human error (such as subjective judgment and visual fatigue). To address these issues, this study proposes a rapid detection method suitable for airborne edge devices and large-scale dense soybean seedling field images. For the dense small target images captured by the Unmanned Aerial Vehicle (UAV), the YOLOv5s model is used as the improvement benchmark in the technical solution. GhostNetV2 is selected as the backbone feature extraction network. In the feature fusion stage, an attention mechanism—Efficient Channel Attention (ECA)—and a Bidirectional Feature Pyramid Network (BiFPN) have been introduced to ensure the model prioritizes the regions of interest. Addressing the challenge of small-scale soybean seedlings in UAV images, the model’s input size is set to 1280 × 1280 pixels. Simultaneously, Performance-aware Approximation of Global Channel Pruning for Multitask CNNs (PAGCP) pruning technology is employed to meet the requirements of mobile or embedded devices. The experimental results show that the identification accuracy of the improved YOLOv5s model reached 92.1%. Compared with the baseline model, its model size and total parameters were reduced by 76.65% and 79.55%, respectively. Beyond these quantitative evaluations, this study also conducted field experiments to verify the detection performance of the improved model in various scenarios. By introducing innovative model structures and technologies, the study aims to effectively detect dense small target features in UAV images and provide a feasible solution for assessing the number of soybean seedlings. In the future, this detection method can also be extended to similar crops.
... The yield prediction method using UAV RGB imagery performed best at the grainfilling stage, which agrees with the previous studies [15,47,48]. This result could be ...
... The yield prediction method using UAV RGB imagery performed best at the grainfilling stage, which agrees with the previous studies [15,47,48]. This result could be explained by the canopy textures at the grain-filling stage being more obvious than those at the flowering stage [46]. ...
... Given the critical roles of the texture features in CNN object recognition [49,50], both Efficientnetv2_s_spw and Efficientnetv2_s_pw achieved improved performance from the flowering stage to the grain-filling stage. In addition, the improved performance of Efficientnetv2_s_spw was also attributed to the considerable contrasts between wheat ears and leaves [46,48,51]. At the grain-filling stage, the wheat ears were green, while the leaves started to turn from green to yellow, presenting considerable contrasts in color. ...
Low-cost UAV RGB imagery combined with deep learning models has demonstrated the potential for the development of a feasible tool for field-scale yield prediction. However, collecting sufficient labeled training samples at the field scale remains a considerable challenge, significantly limiting the practical use. In this study, a split-merge framework was proposed to address the issue of limited training samples at the field scale. Based on the split-merge framework, a yield prediction method for winter wheat using the state-of-the-art Efficientnetv2_s (Efficientnetv2_s_spw) and UAV RGB imagery was presented. In order to demonstrate the effectiveness of the split-merge framework, in this study, Efficientnetv2_s_pw was built by directly feeding the plot images to Efficientnetv2_s. The results indicated that the proposed split-merge framework effectively enlarged the training samples, thus enabling improved yield prediction performance. Efficientnetv2_s_spw performed best at the grain-filling stage, with a coefficient of determination of 0.6341 and a mean absolute percentage error of 7.43%. The proposed split-merge framework improved the model ability to extract indicative image features, partially mitigating the saturation issues. Efficientnetv2_s_spw demonstrated excellent adaptability across the water treatments and was recommended at the grain-filling stage. Increasing the ground resolution of input images may further improve the estimation performance. Alternatively, improved performance may be achieved by incorporating additional data sources, such as the canopy height model (CHM). This study indicates that Efficientnetv2_s_spw is a promising tool for field-scale yield prediction of winter wheat, providing a practical solution to field-specific crop management.
... In addition, with the powerful automatic feature extraction capabilities of convolutional neural networks, recent researchers have adapted methods from other domains and focused on optimizing network structures. These proposed neural networks are generally composed of 2 parts: the first part is the pre-trained module, which is used as a basic feature extractor; the other is an optimized module for the specific task [26][27][28][29]. It provides guaranteed methods for accurate classification and detection with less training images [30]. ...
Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.
... The acquisition of PNPA can help us better evaluate the planting density and improve the yield potential of wheat [3]. The traditional acquisition of PNPA by in situ manual counting is accurate but time-consuming and labor-intensive [4], which seriously limits its application in breeding, precision crop management, and yield estimation. With the development of remote sensing and image processing technologies, it is possible to obtain plot-and field-scale PNPA quickly and accurately in a nondestructive way [4,5]. ...
... The traditional acquisition of PNPA by in situ manual counting is accurate but time-consuming and labor-intensive [4], which seriously limits its application in breeding, precision crop management, and yield estimation. With the development of remote sensing and image processing technologies, it is possible to obtain plot-and field-scale PNPA quickly and accurately in a nondestructive way [4,5]. Therefore, the rapid and accurate estimation of wheat PNPA from remote sensing imagery is important to coordinate yield components, increase yield, and accelerate breeding process. ...
... In the recent decade, since unmanned aerial vehicles (UAV) have strong flexibility in obtaining high spatialtemporal resolution imagery, some researchers have been keen to estimate wheat PNPA from UAV imagery [9,10]. Nevertheless, they mainly used computer vision methods to count wheat panicles from RGB images acquired after heading [4,11]. These methods focusing on the post-heading stages (e.g., anthesis and filling) could contribute to reducing the workload of yield measurements and accelerating the breeding process for breeders [9,11]. ...
Rapid and accurate estimation of panicle number per unit ground area (PNPA) in winter wheat before heading is crucial to evaluate yield potential and regulate crop growth for increasing the final yield. The accuracies of existing methods were low for estimating PNPA with remotely sensed data acquired before heading since the spectral saturation and background effects were ignored. This study proposed a spectral-textural PNPA sensitive index (SPSI) from unmanned aerial vehicle (UAV) multispectral imagery for reducing the spectral saturation and improving PNPA estimation in winter wheat before heading. The effect of background materials on PNPA estimated by textural indices (TIs) was examined, and the composite index SPSI was constructed by integrating the optimal spectral index (SI) and TI. Subsequently, the performance of SPSI was evaluated in comparison with other indices (SI and TIs). The results demonstrated that green-pixel TIs yielded better performances than all-pixel TIs apart from TI[HOM], TI[ENT], and TI[SEM] among all indices from 8 types of textural features. SPSI, which was calculated by the formula DATT[850,730,675] + NDTICOR[850,730], exhibited the highest overall accuracies for any date in any dataset in comparison with DATT[850,730,675], TINDRE[MEA], and NDTICOR[850,730]. For the unified models assembling 2 experimental datasets, the RV2 values of SPSI increased by 0.11 to 0.23, and both RMSE and RRMSE decreased by 16.43% to 38.79% as compared to the suboptimal index on each date. These findings indicated that the SPSI is valuable in reducing the spectral saturation and has great potential to better estimate PNPA using high-resolution satellite imagery.
... Liu et al. [10] proposed an algorithm for counting wheat ears based on K-means clustering of color features, with a recognition accuracy of 94%. Xu et al. [11] automatically extracted the contour features of wheat ears based on the K-means clustering algorithm and later built a Convolutional Neural Network (CNN) model to improve the accuracy of wheat ears recognition to 98.3%. Nevertheless, traditional image processing techniques and machine learning methods still face challenges, such as long recognition segmentation time, low efficiency, and poor complex image recognition segmentation effect [5,12]. ...
... The number of images processed was evenly distributed for each of the three varieties in the dataset. The wheat ears dataset was divided into a training set and a test set in a ratio of 9:1 [11], with 594 images and 66 images in the test set. Finally, the test set was used as the validation set with 66 images. ...
Background
Grain count is crucial to wheat yield composition and estimating yield parameters. However, traditional manual counting methods are time-consuming and labor-intensive. This study developed an advanced deep learning technique for the segmentation counting model of wheat grains. This model has been rigorously tested on three distinct wheat varieties: ‘Bainong 307’, ‘Xinmai 26’, and ‘Jimai 336’, and it has achieved unprecedented predictive counting accuracy.
Method
The images of wheat ears were taken with a smartphone at the late stage of wheat grain filling. We used image processing technology to preprocess and normalize the images to 480*480 pixels. A CBAM-HRNet wheat grain segmentation counting deep learning model based on the Convolutional Block Attention Module (CBAM) was constructed by combining deep learning, migration learning, and attention mechanism. Image processing algorithms and wheat grain texture features were used to build a grain counting and predictive counting model for wheat grains.
Results
The CBAM-HRNet model using the CBAM was the best for wheat grain segmentation. Its segmentation accuracy of 92.04%, the mean Intersection over Union (mIoU) of 85.21%, the category mean pixel accuracy (mPA) of 91.16%, and the recall rate of 91.16% demonstrate superior robustness compared to other models such as HRNet, PSPNet, DeeplabV3+ , and U-Net. Method I for spike count, which calculates twice the number of grains on one side of the spike to determine the total number of grains, demonstrates a coefficient of determination R² of 0.85, a mean absolute error (MAE) of 1.53, and a mean relative error (MRE) of 2.91. In contrast, Method II for spike count involves summing the number of grains on both sides to determine the total number of grains, demonstrating a coefficient of determination R² of 0.92, an MAE) of 1.15, and an MRE) of 2.09%.
Conclusions
Image segmentation algorithm of the CBAM-HRNet wheat spike grain is a powerful solution that uses the CBAM to segment wheat spike grains and obtain richer semantic information. This model can effectively address the challenges of small target image segmentation and under-fitting problems in training. Additionally, the spike grain counting model can quickly and accurately predict the grain count of wheat, providing algorithmic support for efficient and intelligent wheat yield estimation.
... The object detector-based approach has had many studies applying machine learning methods to implement. In particular, research [2] has been performed to determine the total number of wheat grains on an actual photograph in the field by applying the K-mean clustering algorithm to extract the morphological and color features of the plant wheat with accuracy up to 98.5%. Research [3,4] uses machine learning, and Yolo shows relatively high accuracy on the data set. ...
Counting the number of rice seeds is essential in assessing the quality of rice varieties such as yield, rice diseases (sloppy rice), etc. Besides the machine learning approach, there are disadvantages. Contrary to machine learning, the counting approach utilizes image processing. This research contributes to counting the number of rice grains per panicle by combining contrast limited adaptive histogram equalization and Candy algorithm on a data set of 150 rice samples. The method is conducted through the steps of denoising, converting RGB color channels to LAB, image segmentation and contouring, and finally counting. The study’s results were evaluated by comparing counting by hand and by algorithm; the error result was ~0.902%.
