High-Throughput Rice Density Estimation from Transplantation to Tillering Stages Using Deep Networks

Liu, Liang; Lu, Hao; Li, Yanan; Cao, Zhiguo

doi:10.34133/2020/1375957

Cited by 28 publications

(18 citation statements)

References 43 publications

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“…The integral of the density map is equal to the total number of objects. Inspired by the success of these methods in crowd counting, a constellation of methods (Lu et al, 2017b;Xiong et al, 2019a;Liu et al, 2020) and datasets (David et al, 2020;Lu et al, 2021) are proposed for plant counting. However, existing plant counting methods neglect the influence of domain gap, which is common in real applications.…”

Section: Related Workmentioning

confidence: 99%

“…We evaluated the performance of UDA on three public plant counting datasets: Maize Tassel Counting (MTC) dataset (Lu et al, 2017b), Rice Plant Counting (RPC) dataset (Liu et al, 2020) and Maize Tassel Counting UAV (MTC-UAV) (Lu et al, 2021) dataset. Here, we briefly introduce the statistics and characteristics of these datasets.…”

Section: Plant Counting Datasetsmentioning

confidence: 99%

“…Estimating the number of plants accurately and efficiently is an important task in agriculture breeding and plant phenotyping. Counting plants (Liu et al, 2020) or their flowers (Lu et al, 2017b) and fruits (Bargoti and Underwood, 2017) can help farmers to monitor the status of crops and estimate yield. Recently, deep learning-based object counting models (Zhang et al, 2015), which directly infer object counts from a single image, can be a promising choice for plant counting.…”

Section: Introductionmentioning

confidence: 99%

“…We evaluated our method on three public datasets: MTC (Lu et al, 2017b), RPC (Liu et al, 2020) and MTC-UAV (Lu et al, 2021), including 7 different and representative domain adaptation settings close to real applications. We split data into different domains by different cultivars, locations, and image acquisition devices.…”

Section: Introductionmentioning

confidence: 99%

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Background-Aware Domain Adaptation for Plant Counting

Shi

Lü

et al. 2022

Front. Plant Sci.

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Deep learning-based object counting models have recently been considered preferable choices for plant counting. However, the performance of these data-driven methods would probably deteriorate when a discrepancy exists between the training and testing data. Such a discrepancy is also known as the domain gap. One way to mitigate the performance drop is to use unlabeled data sampled from the testing environment to correct the model behavior. This problem setting is also called unsupervised domain adaptation (UDA). Despite UDA has been a long-standing topic in machine learning society, UDA methods are less studied for plant counting. In this paper, we first evaluate some frequently-used UDA methods on the plant counting task, including feature-level and image-level methods. By analyzing the failure patterns of these methods, we propose a novel background-aware domain adaptation (BADA) module to address the drawbacks. We show that BADA can easily fit into object counting models to improve the cross-domain plant counting performance, especially on background areas. Benefiting from learning where to count, background counting errors are reduced. We also show that BADA can work with adversarial training strategies to further enhance the robustness of counting models against the domain gap. We evaluated our method on 7 different domain adaptation settings, including different camera views, cultivars, locations, and image acquisition devices. Results demonstrate that our method achieved the lowest Mean Absolute Error on 6 out of the 7 settings. The usefulness of BADA is also supported by controlled ablation studies and visualizations.

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Section: Related Workmentioning

confidence: 99%

Section: Plant Counting Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Background-Aware Domain Adaptation for Plant Counting

Shi

Lü

et al. 2022

Front. Plant Sci.

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“…Plant counting is regarded as an object-counting task in the computer vision area (Lu and Cao, 2020). For example, Liu et al (2020a) counted rice to estimate density using the deep learning method. However, these methods discard location information of the plant and the poor explainability limits the counting performances (Lu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Automatic counting of rapeseed inflorescences using deep learning method and UAV RGB imagery

Li²,

Qiao³

et al. 2023

Front. Plant Sci.

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Flowering is a crucial developing stage for rapeseed (Brassica napus L.) plants. Flowers develop on the main and branch inflorescences of rapeseed plants and then grow into siliques. The seed yield of rapeseed heavily depends on the total flower numbers per area throughout the whole flowering period. The number of rapeseed inflorescences can reflect the richness of rapeseed flowers and provide useful information for yield prediction. To count rapeseed inflorescences automatically, we transferred the counting problem to a detection task. Then, we developed a low-cost approach for counting rapeseed inflorescences using YOLOv5 with the Convolutional Block Attention Module (CBAM) based on unmanned aerial vehicle (UAV) Red–Green–Blue (RGB) imagery. Moreover, we constructed a Rapeseed Inflorescence Benchmark (RIB) to verify the effectiveness of our model. The RIB dataset captured by DJI Phantom 4 Pro V2.0, including 165 plot images and 60,000 manual labels, is to be released. Experimental results showed that indicators R2 for counting and the mean Average Precision (mAP) for location were over 0.96 and 92%, respectively. Compared with Faster R-CNN, YOLOv4, CenterNet, and TasselNetV2+, the proposed method achieved state-of-the-art counting performance on RIB and had advantages in location accuracy. The counting results revealed a quantitative dynamic change in the number of rapeseed inflorescences in the time dimension. Furthermore, a significant positive correlation between the actual crop yield and the automatically obtained rapeseed inflorescence total number on a field plot level was identified. Thus, a set of UAV- assisted methods for better determination of the flower richness was developed, which can greatly support the breeding of high-yield rapeseed varieties.

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Rape seedling density estimation in‐field conditions based on improved multi‐column convolutional neural network

et al. 2023

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Early‐stage rape seedling density is closely related to yield estimation, growth diagnosis, cultivated area statistics, and field management. Currently, manual sampling and counting, which are inefficient and inaccurate, are heavily relied upon to estimate rape seedling density. Computer vision techniques have emerged as a promising solution to the automation of this task, as digital images have become more commonplace. Farmland field environments, however, face many challenges, including scale variation, denseness, and background occlusion. An improved multi‐column convolutional neural network, called seedling rape density prediction network (SRDPNet), has been proposed in this study to resolve the issues related to accurate density estimation and counting of rape seedlings in complex farmland scenarios. Based on the multi‐column convolutional attention encoder, filters of different sizes are used to capture the basic feature of rape seedlings at various scales. The channel attention and position attention modules are introduced into branches to alleviate the impact of low counting accuracy caused by background error and growth state differences. The SRDPNet was validated using the seedling rapeseed plant counting (SRPC) dataset created in this study. The experimental results showed that the SRDPNet demonstrated high accurate counting performance for the SRPC dataset with a high coefficient of determination (R2 = 0.97396) and mean absolute error (MAE = 3.26, mean square error = 4.56), which are superior to that of the comparison method. SRDPNet can effectively solve the visual challenges of rape seedlings in complex farmland scenes and improve the robustness for complex visual variations.

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High-Throughput Rice Density Estimation from Transplantation to Tillering Stages Using Deep Networks

Cited by 28 publications

References 43 publications

Background-Aware Domain Adaptation for Plant Counting

Background-Aware Domain Adaptation for Plant Counting

Automatic counting of rapeseed inflorescences using deep learning method and UAV RGB imagery

Rape seedling density estimation in‐field conditions based on improved multi‐column convolutional neural network

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