TasselNetV3: Explainable Plant Counting With Guided Upsampling and Background Suppression

Lü, Hao; Liu, Liang; Li, Yanan; Zhao, Xiaoming; Wang, Xiqing

doi:10.1109/tgrs.2021.3058962

Cited by 32 publications

(30 citation statements)

References 49 publications

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“…For in-field plant counting tasks (from RGB images), deep learning-based methods show great robustness against different illuminations, scales and complex backgrounds (Lu et al, 2017b). The release of datasets (David et al, 2020;Lu et al, 2021) also accelerates the development of deep learning-based plant counting methods. Therefore, the deep learning has become the default choice for in-field plant counting.…”

Section: Related Workmentioning

confidence: 99%

“…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%

“…Adding a segmentation branch can guide the network to learn where to count (Lu et al, 2021;Modolo et al, 2021). Nevertheless, under the cross-domain setting, the segmentation branch also suffers from the domain gap.…”

Section: Discriminatormentioning

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: Related Workmentioning

confidence: 99%

Section: Plant Counting Datasetsmentioning

confidence: 99%

Section: Discriminatormentioning

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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Counting spikelets from infield wheat crop images using fully convolutional networks

Alkhudaydi

lglesia

2022

Neural Comput & Applic

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Wheat is one of the world's three main crops, with global consumption projected to reach more than 850 million tons by 2050. Stabilizing yield and quality of wheat cultivation is a major issue. With the use of remote sensing and non-invasive imaging technology, the internet of things (IoT) has allowed us to constantly monitor crop development in agriculture. The output of such technologies may be analyzed using machine-learning algorithms and image processing methods to extract useful information for crop management assistance. Counting wheat spikelets from infield images is considered one of the challenges related to estimating yield traits of wheat crops. For this challenging problem, we propose a density estimation approach related to crowd counting, SpikeCount. Our proposed counting methods are based on deep learning architectures as those have the advantage of being able to identify features automatically. Annotation of images with the ground truth are required for machine learning approaches, but those are expensive in terms of time and resources. We use Transfer Learning in both tasks, segmentation and counting. Our results indicate the segmentation is beneficial as focusing only on the regions of interest improves counting accuracy in most scenarios. In particular, Transfer Learning from similar images produced excellent results for the counting task for most of the stages of wheat development.

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Automatic counting of lettuce using an improved YOLOv5s with multiple lightweight strategies

Zhang¹,

Li²

2023

Expert Systems with Applications

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TasselNetV3: Explainable Plant Counting With Guided Upsampling and Background Suppression

Cited by 32 publications

References 49 publications

Background-Aware Domain Adaptation for Plant Counting

Background-Aware Domain Adaptation for Plant Counting

Counting spikelets from infield wheat crop images using fully convolutional networks

Automatic counting of lettuce using an improved YOLOv5s with multiple lightweight strategies

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