Abstract
BackgroundFlowering is one of the most important processes for flowering plants such as cotton, reflecting the transition from vegetative to reproductive growth and is of central importance to crop yield and adaptability. Conventionally, categorical scoring systems have been widely used to study flowering patterns, which are laborious and subjective to apply. The goal of this study was to develop a deep learning-based approach to characterize flowering patterns for cotton plants that flower progressively over several weeks, with flowers distributed across much of the plant.ResultsA ground mobile system (GPhenoVision) was modified with a multi-view color imaging module, to acquire images of a plant from four viewing angles at a time. A total of 116 plants from 23 genotypes were imaged during an approximately 2-month period with an average scanning interval of 2–3 days, yielding a dataset containing 8666 images. A subset (475) of the images were randomly selected and manually annotated to form datasets for training and selecting the best object detection model. With the best model, a deep learning-based approach (DeepFlower) was developed to detect and count individual emerging blooms for a plant on a given date. The DeepFlower was used to process all images to obtain bloom counts for individual plants over the flowering period, using the resulting counts to derive flowering curves (and thus flowering characteristics). Regression analyses showed that the DeepFlower method could accurately (R2 = 0.88 and RMSE = 0.79) detect and count emerging blooms on cotton plants, and statistical analyses showed that imaging-derived flowering characteristics had similar effectiveness as manual assessment for identifying differences among genetic categories or genotypes.ConclusionsThe developed approach could thus be an effective and efficient tool to characterize flowering patterns for flowering plants (such as cotton) with complex canopy architecture.
Highlights
Flowering is one of the most important processes for flowering plants such as cotton, reflecting the transition from vegetative to reproductive growth and is of central importance to crop yield and adaptability
Cotton plants were branchy and leafy during the flowering period, so blooms were frequently occluded by plant leaves and branches
The FrRCNN5-cls model learned effective features to describe and detect occluded emerging blooms, especially some heavily occluded emerging blooms (Fig. 1f ). All of these successful cases demonstrated the capability of the FrRCNN5-cls model to detect plants and emerging blooms in images with varied conditions
Summary
Flowering is one of the most important processes for flowering plants such as cotton, reflecting the transition from vegetative to reproductive growth and is of central importance to crop yield and adaptability. Categorical scoring systems have been widely used to study flowering patterns, which are laborious and subjective to apply. Flowering is one of the most important processes for angiosperms (flowering plants), reflecting the transition from vegetative to reproductive growth and significantly affecting crop yield and adaptability to various. Studies related to plant flowering patterns have required human evaluators to check experiment fields and record flowering status manually. Human evaluators often used a categorical scoring system to assess flowering stages (e.g., estimating when 10% of plants in a plot have opened blooms), so that the time duration between particular flowering stages can be calculated. An automated high throughput approach to characterize flowering patterns can mitigate each of these disadvantages
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