WheatNet-Lite: A Novel Light Weight Network for Wheat Head Detection

Bhagat, Sandesh; Kokare, Manesh; Haswani, Vineet; Hambarde, Praful; Kamble, Ravi

doi:10.1109/iccvw54120.2021.00154

Cited by 31 publications

(16 citation statements)

References 19 publications

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“…The Global Wheat Detection (GWHD) dataset ( David et al, 2020 ) was a standard image set collected by several research institutions, which was considered by many scholars as a new challenge for wheat spike detection. Bhagat et al (2021) proposed a novel WheatNet-Lite network, which was solved the dense and overlapping wheat spikes. The network was validated on GWHD, SPIKE, and ACID datasets.…”

Section: Introductionmentioning

confidence: 99%

Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet

Cheng

Chen

et al. 2022

Front. Plant Sci.

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The number of wheat spikes per unit area is one of the most important agronomic traits associated with wheat yield. However, quick and accurate detection for the counting of wheat spikes faces persistent challenges due to the complexity of wheat field conditions. This work has trained a RetinaNet (SpikeRetinaNet) based on several optimizations to detect and count wheat spikes efficiently. This RetinaNet consists of several improvements. First, a weighted bidirectional feature pyramid network (BiFPN) was introduced into the feature pyramid network (FPN) of RetinaNet, which could fuse multiscale features to recognize wheat spikes in different varieties and complicated environments. Then, to detect objects more efficiently, focal loss and attention modules were added. Finally, soft non-maximum suppression (Soft-NMS) was used to solve the occlusion problem. Based on these improvements, the new network detector was created and tested on the Global Wheat Head Detection (GWHD) dataset supplemented with wheat-wheatgrass spike detection (WSD) images. The WSD images were supplemented with new varieties of wheat, which makes the mixed dataset richer in species. The method of this study achieved 0.9262 for mAP50, which improved by 5.59, 49.06, 2.79, 1.35, and 7.26% compared to the state-of-the-art RetinaNet, single-shot multiBox detector (SSD), You Only Look Once version3 (Yolov3), You Only Look Once version4 (Yolov4), and faster region-based convolutional neural network (Faster-RCNN), respectively. In addition, the counting accuracy reached 0.9288, which was improved from other methods as well. Our implementation code and partial validation data are available at https://github.com/wujians122/The-Wheat-Spikes-Detecting-and-Counting.

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Section: Introductionmentioning

confidence: 99%

Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet

Cheng

Chen

et al. 2022

Front. Plant Sci.

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“…The availability of a large-scale and diverse dataset like the Global Wheat Head Detection (GWHD) dataset [13] has enabled researchers to develop novel supervised deep learning-based methods for detecting and counting wheat heads from field images [11,14,15]. However, the assessment of many important plant traits, such as organ size, organ health, biotic and abiotic stress, requires fine-grain semantic segmentation of plant organs.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning models have shown promising results in various computer vision tasks, including object recognition [1], object detection [2], instance segmentation [3], and semantic segmentation [4]. Deep learning has shown the potential to be widely utilized in precision agriculture, which focuses on computational methods to sustainably improve the quality and quantity of crop production [5][6][7][8][9][10][11][12]. The availability of a large-scale and diverse dataset like the Global Wheat Head Detection (GWHD) dataset [13] has enabled researchers to develop novel supervised deep learning-based methods for detecting and counting wheat heads from field images [11,14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning has shown the potential to be widely utilized in precision agriculture, which focuses on computational methods to sustainably improve the quality and quantity of crop production [5][6][7][8][9][10][11][12]. The availability of a large-scale and diverse dataset like the Global Wheat Head Detection (GWHD) dataset [13] has enabled researchers to develop novel supervised deep learning-based methods for detecting and counting wheat heads from field images [11,14,15]. However, the assessment of many important plant traits, such as organ size, organ health, biotic and abiotic stress, requires fine-grain semantic segmentation of plant organs.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns

Najafian

Ghanbari

Kish

et al. 2022

Preprint

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Deep learning has shown potential in domains where large-scale annotated datasets are available. However, manual annotation is expensive, time-consuming, and tedious. Pixel-level annotations are particularly costly for semantic segmentation in images with dense irregular patterns of object instances, such as in plant images. In this work, we propose a method for developing high-performing deep learning models for semantic segmentation of wheat heads utilizing little manual annotation. We simulate a computationally-annotated dataset using a few annotated images, a short unannotated video clip of a wheat field, and several video clips from fields with no wheat. This dataset is then used to train a customized U-Net model for wheat head segmentation. Considering the distribution shift between the simulated and real data, we apply three domain adaptation steps to gradually bridge the domain gap. Only using two annotated images, we achieved a Dice score of 0.89 on the internal test set, i.e., images extracted from the wheat field video. The model trained using only two annotated images was evaluated on a diverse external dataset collected from 18 different domains across five countries and achieved a Dice score of 0.73. To further expose the model to images from different growth stages and environmental conditions, we incorporated two annotated images from each of the 18 domains and further fine-tuned the model. This resulted in improving the Dice score to 0.91. These promising results highlight the utility of the proposed approach in the absence of large-annotated datasets. Although the utility of the proposed method is shown on a wheat head dataset, it can be extended to other segmentation tasks with similar characteristics of irregularly repeating patterns of object instances.

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