Dataset Information

Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice.

ABSTRACT: Rice (Oryza sativa) is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties' yield performance, key yield-related traits such as panicle number per unit area (PNpM²) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM² trait due to complex field conditions, a large variation of rice cultivars, and their panicle morphological features. Here, we present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery. To facilitate the development of AI-powered detection models, we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists; then, we integrated several state-of-the-art deep learning models (including a preferred model called Panicle-AI) into the Panicle-Cloud platform, so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images. We trialed the AI models with images collected at different attitudes and growth stages, through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified. Then, we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM² trait from hundreds of rice varieties. Through correlation analysis between computational analysis and manual scoring, we found that the platform could quantify the PNpM² trait reliably, based on which yield production was classified with high accuracy. Hence, we trust that our work demonstrates a valuable advance in phenotyping the PNpM² trait in rice, which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.

SUBMITTER: Teng Z

PROVIDER: S-EPMC10578299 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice.

Teng Zixuan Z Chen Jiawei J Wang Jian J Wu Shuixiu S Chen Riqing R Lin Yaohai Y Shen Liyan L Jackson Robert R Zhou Ji J Yang Changcai C

Plant phenomics (Washington, D.C.) 20231016

Rice (<i>Oryza sativa</i>) is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties' yield performance, key yield-related traits such as panicle number per unit area (PNpM<sup>2</sup>) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicl ...[more]

PMID: 37850120

Similar Datasets

Project description:Wheat (Triticum aestivum) is one of the most important staple crops worldwide. To ensure its global supply, the timing and duration of its growth cycle needs to be closely monitored in the field so that necessary crop management activities can be arranged in a timely manner. Also, breeders and plant researchers need to evaluate growth stages (GSs) for tens of thousands of genotypes at the plot level, at different sites and across multiple seasons. These indicate the importance of providing a reliable and scalable toolkit to address the challenge so that the plot-level assessment of GS can be successfully conducted for different objectives in plant research. Here, we present a multimodal deep learning model called GSP-AI, capable of identifying key GSs and predicting the vegetative-to-reproductive transition (i.e., flowering days) in wheat based on drone-collected canopy images and multiseasonal climatic datasets. In the study, we first established an open Wheat Growth Stage Prediction (WGSP) dataset, consisting of 70,410 annotated images collected from 54 varieties cultivated in China, 109 in the United Kingdom, and 100 in the United States together with key climatic factors. Then, we built an effective learning architecture based on Res2Net and long short-term memory (LSTM) to learn canopy-level vision features and patterns of climatic changes between 2018 and 2021 growing seasons. Utilizing the model, we achieved an overall accuracy of 91.2% in identifying key GS and an average root mean square error (RMSE) of 5.6 d for forecasting the flowering days compared with manual scoring. We further tested and improved the GSP-AI model with high-resolution smartphone images collected in the 2021/2022 season in China, through which the accuracy of the model was enhanced to 93.4% for GS and RMSE reduced to 4.7 d for the flowering prediction. As a result, we believe that our work demonstrates a valuable advance to inform breeders and growers regarding the timing and duration of key plant growth and development phases at the plot level, facilitating them to conduct more effective crop selection and make agronomic decisions under complicated field conditions for wheat improvement.

Project description:As one of the most consumed stable foods around the world, wheat plays a crucial role in ensuring global food security. The ability to quantify key yield components under complex field conditions can help breeders and researchers assess wheat's yield performance effectively. Nevertheless, it is still challenging to conduct large-scale phenotyping to analyse canopy-level wheat spikes and relevant performance traits, in the field and in an automated manner. Here, we present CropQuant-Air, an AI-powered software system that combines state-of-the-art deep learning (DL) models and image processing algorithms to enable the detection of wheat spikes and phenotypic analysis using wheat canopy images acquired by low-cost drones. The system includes the YOLACT-Plot model for plot segmentation, an optimised YOLOv7 model for quantifying the spike number per m2 (SNpM2) trait, and performance-related trait analysis using spectral and texture features at the canopy level. Besides using our labelled dataset for model training, we also employed the Global Wheat Head Detection dataset to incorporate varietal features into the DL models, facilitating us to perform reliable yield-based analysis from hundreds of varieties selected from main wheat production regions in China. Finally, we employed the SNpM2 and performance traits to develop a yield classification model using the Extreme Gradient Boosting (XGBoost) ensemble and obtained significant positive correlations between the computational analysis results and manual scoring, indicating the reliability of CropQuant-Air. To ensure that our work could reach wider researchers, we created a graphical user interface for CropQuant-Air, so that non-expert users could readily use our work. We believe that our work represents valuable advances in yield-based field phenotyping and phenotypic analysis, providing useful and reliable toolkits to enable breeders, researchers, growers, and farmers to assess crop-yield performance in a cost-effective approach.

Project description:The number of panicles per unit area (PNpA) is one of the key factors contributing to the grain yield of rice crops. Accurate PNpA quantification is vital for breeding high-yield rice cultivars. Previous studies were based on proximal sensing with fixed observation platforms or unmanned aerial vehicles (UAVs). The near-canopy images produced in these studies suffer from inefficiency and complex image processing pipelines that require manual image cropping and annotation. This study aims to develop an automated, high-throughput UAV imagery-based approach for field plot segmentation and panicle number quantification, along with a novel classification method for different panicle types, enhancing PNpA quantification at the plot level. RGB images of the rice canopy were efficiently captured at an altitude of 15 m, followed by image stitching and plot boundary recognition via a mask region-based convolutional neural network (Mask R-CNN). The images were then segmented into plot-scale subgraphs, which were categorized into 3 growth stages. The panicle vision transformer (Panicle-ViT), which integrates a multipath vision transformer and replaces the Mask R-CNN backbone, accurately detects panicles. Additionally, the Res2Net50 architecture classified panicle types with 4 angles of 0°, 15°, 45°, and 90°. The results confirm that the performance of Plot-Seg is comparable to that of manual segmentation. Panicle-ViT outperforms the traditional Mask R-CNN across all the datasets, with the average precision at 50% intersection over union (AP50) improved by 3.5% to 20.5%. The PNpA quantification for the full dataset achieved superior performance, with a coefficient of determination (R 2) of 0.73 and a root mean square error (RMSE) of 28.3, and the overall panicle classification accuracy reached 94.8%. The proposed approach enhances operational efficiency and automates the process from plot cropping to PNpA prediction, which is promising for accelerating the selection of desired traits in rice breeding.

Dataset Information

Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice.

Publications

Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets