DeepPhenology: Estimation of apple flower phenology distributions based on deep learning

Wang, Xu; Tang, Julie; Whitty, Mark

doi:10.1016/j.compag.2021.106123

Cited by 46 publications

(23 citation statements)

References 20 publications

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“…All data were collected within one day. In a second study, Wang et al ( 2021 ) used the same method for data acquisition, but data were collected not only on one but throughout 26 days to classify different flower phenology stages and their distribution on the tree. The flowering stage was also investigated by Pahalawatta et al ( 2020 ).…”

Section: Resultsmentioning

confidence: 99%

Deep Learning in Plant Phenological Research: A Systematic Literature Review

et al. 2022

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Climate change represents one of the most critical threats to biodiversity with far-reaching consequences for species interactions, the functioning of ecosystems, or the assembly of biotic communities. Plant phenology research has gained increasing attention as the timing of periodic events in plants is strongly affected by seasonal and interannual climate variation. Recent technological development allowed us to gather invaluable data at a variety of spatial and ecological scales. The feasibility of phenological monitoring today and in the future depends heavily on developing tools capable of efficiently analyzing these enormous amounts of data. Deep Neural Networks learn representations from data with impressive accuracy and lead to significant breakthroughs in, e.g., image processing. This article is the first systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research. In a multi-stage process, we selected 24 peer-reviewed studies published in the last five years (2016–2021). After carefully analyzing these studies, we describe the applied methods categorized according to the studied phenological stages, vegetation type, spatial scale, data acquisition- and deep learning methods. Furthermore, we identify and discuss research trends and highlight promising future directions. We present a systematic overview of previously applied methods on different tasks that can guide this emerging complex research field.

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Section: Resultsmentioning

confidence: 99%

Deep Learning in Plant Phenological Research: A Systematic Literature Review

et al. 2022

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“…Image‐based setups coupled with computer vision techniques are increasingly being suggested for monitoring flowers, especially in agricultural settings (Jiang et al., 2020; Palacios et al., 2020; Wang et al., 2021). Such methods can facilitate automated and efficient assessment of phenology and crop yield forecasting, but often requires human camera operators of custom‐made mobile imaging platforms.…”

Section: Discussionmentioning

confidence: 99%

Automatic flower detection and phenology monitoring using time‐lapse cameras and deep learning

Mann

Iosifidis

Jepsen

et al. 2022

Remote Sens Ecol Conserv

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The advancement of spring is a widespread biological response to climate change observed across taxa and biomes. However, the species level responses to warming are complex and the underlying mechanisms are difficult to disentangle. This is partly due to a lack of data, which are typically collected by direct observations, and thus very time-consuming to obtain. Data deficiency is especially pronounced in the Arctic where the warming is particularly severe. We present a method for automated monitoring of flowering phenology of specific plant species at very high temporal resolution through full growing seasons and across geographical regions. The method consists of image-based monitoring of field plots using near-surface time-lapse cameras and subsequent automated detection and counting of flowers in the images using a convolutional neural network. We demonstrate the feasibility of collecting flower phenology data using automatic time-lapse cameras and show that the temporal resolution of the results surpasses what can be collected by traditional observation methods. We focus on two Arctic species, the mountain avens Dryas octopetala and Dryas integrifolia in 20 image series from four sites. Our flower detection model proved capable of detecting flowers of the two species with a remarkable precision of 0.918 (adjusted to 0.966) and a recall of 0.907. Thus, the method can automatically quantify the seasonal dynamics of flower abundance at fine scale and return reliable estimates of traditional phenological variables such as the timing of onset, peak, and end of flowering. We describe the system and compare manual and automatic extraction of flowering phenology data from the images. Our method can be directly applied on sites containing mountain avens using our trained model, or the model could be fine-tuned to other species. We discuss the potential of automatic image-based monitoring of flower phenology and how the method can be improved and expanded for future studies.

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“…Therefore, it is necessary to optimize and improve the existing network model to meet the detection needs of this research. Currently, object detection technology has been used in strawberries [30][31][32], grapes [33], apple fruits [34,35], flowers [36], maize [25], and rice [37,38], and has achieved relatively good application results. There is scientific evidence about the accuracy of the proposed architecture described in the studies by Li et al [39], in which YOLO-JD has achieved the best detection accuracy, with an average mAP of 96.63%.…”

Section: Related Workmentioning

confidence: 99%

Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

et al. 2022

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Traditional plant disease diagnosis methods are mostly based on expert diagnosis, which easily leads to the backwardness of crop disease control and field management. In this paper, to improve the speed and accuracy of disease classification, a plant disease detection and classification method based on the optimized lightweight YOLOv5 model is proposed. We propose an IASM mechanism to improve the accuracy and efficiency of the model, to achieve model weight reduction through Ghostnet and WBF structure, and to combine BiFPN and fast normalization fusion for weighted feature fusion to speed up the learning efficiency of each feature layer. To verify the effect of the optimized model, we conducted a performance comparison test and ablation test between the optimized model and other mainstream models. The results show that the operation time and accuracy of the optimized model are 11.8% and 3.98% higher than the original model, respectively, while F1 score reaches 92.65%, which highlight statistical metrics better than the current mainstream models. Moreover, the classification accuracy rate on the self-made dataset reaches 92.57%, indicating the effectiveness of the plant disease classification model proposed in this paper, and the transfer learning ability of the model can be used to expand the application scope in the future.

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DeepPhenology: Estimation of apple flower phenology distributions based on deep learning

Cited by 46 publications

References 20 publications

Deep Learning in Plant Phenological Research: A Systematic Literature Review

Deep Learning in Plant Phenological Research: A Systematic Literature Review

Automatic flower detection and phenology monitoring using time‐lapse cameras and deep learning

Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

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