TasselNetV2+: A Fast Implementation for High-Throughput Plant Counting From High-Resolution RGB Imagery

Lü, Hao

doi:10.3389/fpls.2020.541960

Cited by 55 publications

(37 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hasan et al (2018) adopted R-CNN (Girshick et al, 2014) and Madec et al (2019) adopted the Faster-RCNN (Ren et al, 2017) method to calculate the number of wheat ears. Later, more researchers utilized object detection methods to model wheat ear counting tasks (Mohanty et al, 2016;Xiong et al, 2019;Lu and Cao, 2020). Therefore, wheat ear counting based on deep learning was realized by object detection methods, which makes the algorithm easy to be applied in practice.…”

Section: Introductionmentioning

confidence: 99%

Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning

2021

View full text Add to dashboard Cite

Counting the number of wheat ears in images under natural light is an important way to evaluate the crop yield, thus, it is of great significance to modern intelligent agriculture. However, the distribution of wheat ears is dense, so the occlusion and overlap problem appears in almost every wheat image. It is difficult for traditional image processing methods to solve occlusion problem due to the deficiency of high-level semantic features, while existing deep learning based counting methods did not solve the occlusion efficiently. This article proposes an improved EfficientDet-D0 object detection model for wheat ear counting, and focuses on solving occlusion. First, the transfer learning method is employed in the pre-training of the model backbone network to extract the high-level semantic features of wheat ears. Secondly, an image augmentation method Random-Cutout is proposed, in which some rectangles are selected and erased according to the number and size of the wheat ears in the images to simulate occlusion in real wheat images. Finally, convolutional block attention module (CBAM) is adopted into the EfficientDet-D0 model after the backbone, which makes the model refine the features, pay more attention to the wheat ears and suppress other useless background information. Extensive experiments are done by feeding the features to detection layer, showing that the counting accuracy of the improved EfficientDet-D0 model reaches 94%, which is about 2% higher than the original model, and false detection rate is 5.8%, which is the lowest among comparative methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning

2021

View full text Add to dashboard Cite

show abstract

“…The current application of drones combined with deep learning technology has greatly promoted the development of precision agriculture. In recent years, some meaningful research [ 7 , 8 , 9 , 15 , 27 , 28 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ] has emerged. These studies have used RGB (red, green, blue), multispectral, hyperspectral, and thermal infrared data acquired by UAV and CNN to evaluate the phenotypic characteristics of citrus crops [ 38 ], obtain key points of plants/plant leaves [ 39 ], plant stress analysis and plant disease identification [ 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

Wheat Ear Recognition Based on RetinaNet and Transfer Learning

Fei

et al. 2021

Sensors

View full text Add to dashboard Cite

The number of wheat ears is an essential indicator for wheat production and yield estimation, but accurately obtaining wheat ears requires expensive manual cost and labor time. Meanwhile, the characteristics of wheat ears provide less information, and the color is consistent with the background, which can be challenging to obtain the number of wheat ears required. In this paper, the performance of Faster regions with convolutional neural networks (Faster R-CNN) and RetinaNet to predict the number of wheat ears for wheat at different growth stages under different conditions is investigated. The results show that using the Global WHEAT dataset for recognition, the RetinaNet method, and the Faster R-CNN method achieve an average accuracy of 0.82 and 0.72, with the RetinaNet method obtaining the highest recognition accuracy. Secondly, using the collected image data for recognition, the R2 of RetinaNet and Faster R-CNN after transfer learning is 0.9722 and 0.8702, respectively, indicating that the recognition accuracy of the RetinaNet method is higher on different data sets. We also tested wheat ears at both the filling and maturity stages; our proposed method has proven to be very robust (the R2 is above 90). This study provides technical support and a reference for automatic wheat ear recognition and yield estimation.

show abstract

“…However, this method might be less robust in the later growth stage than object detector (Madec et al, 2019). TasselNetV2 and TasselNetV2 + was subsequently proposed by the same research group to improve the counting accuracy and efficiency (Xiong et al, 2019;Lu and Cao, 2020). Compared with other deep convolution neural networks, TasselNetV2 + reduced the use of the video memory and would be able to analyze large size images efficiently.…”

Section: Introductionmentioning

confidence: 99%

Field rice panicle detection and counting based on deep learning

Wang

Yang

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

Panicle number is directly related to rice yield, so panicle detection and counting has always been one of the most important scientific research topics. Panicle counting is a challenging task due to many factors such as high density, high occlusion, and large variation in size, shape, posture et.al. Deep learning provides state-of-the-art performance in object detection and counting. Generally, the large images need to be resized to fit for the video memory. However, small panicles would be missed if the image size of the original field rice image is extremely large. In this paper, we proposed a rice panicle detection and counting method based on deep learning which was especially designed for detecting rice panicles in rice field images with large image size. Different object detectors were compared and YOLOv5 was selected with MAPE of 3.44% and accuracy of 92.77%. Specifically, we proposed a new method for removing repeated detections and proved that the method outperformed the existing NMS methods. The proposed method was proved to be robust and accurate for counting panicles in field rice images of different illumination, rice accessions, and image input size. Also, the proposed method performed well on UAV images. In addition, an open-access and user-friendly web portal was developed for rice researchers to use the proposed method conveniently.

show abstract

TasselNetV2+: A Fast Implementation for High-Throughput Plant Counting From High-Resolution RGB Imagery

Cited by 55 publications

References 55 publications

Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning

Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning

Wheat Ear Recognition Based on RetinaNet and Transfer Learning

Field rice panicle detection and counting based on deep learning

Contact Info

Product

Resources

About