ArticlePlant methods2019
Automatic estimation of heading date of paddy rice using deep learning.
Article in Plant methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
- OPAL-Flow: Orientation-aware rice panicle detection and minute-scale anthesis rhythm identification under field conditions.Plant phenomics (Washington, D.C.) · 2026Article
- Machine learning assisted analysis of rice flower opening times using a low-cost time-lapse camera.Journal of plant research · 2025Article
- Phenology analysis for trait prediction using UAVs in a MAGIC rice population with different transplanting protocols.Frontiers in artificial intelligence · 2024Article
- High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits.Frontiers in plant science · 2024Article
- Investigating the contribution of image time series observations to cauliflower harvest-readiness prediction.Frontiers in artificial intelligence · 2024Article
- Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data.Sensors (Basel, Switzerland) · 2023Article
- Analyzing Nitrogen Effects on Rice Panicle Development by Panicle Detection and Time-Series Tracking.Plant phenomics (Washington, D.C.) · 2023Article
- Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income.Plant phenomics (Washington, D.C.) · 2023Article
- Explainable deep learning in plant phenotyping.Frontiers in artificial intelligence · 2023Review
- Image-Based Phenotyping for Non-Destructive In Situ Rice (Sensors (Basel, Switzerland) · 2022Article
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- How Useful Is Image-Based Active Learning for Plant Organ Segmentation?Plant phenomics (Washington, D.C.) · 2022Article
- Field rice panicle detection and counting based on deep learning.Frontiers in plant science · 2022Article
- SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods.Plant phenomics (Washington, D.C.) · 2022Article
- Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice.Frontiers in plant science · 2022Article
- Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model.Frontiers in plant science · 2022Article
- Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season.Plant methods · 2021Article
- Scaling up high-throughput phenotyping for abiotic stress selection in the field.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Review
- High-throughput soybean seeds phenotyping with convolutional neural networks and transfer learning.Plant methods · 2021Article
- Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping.Plant methods · 2021Article
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Authors and funding
5 authors.
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Abstract
backgroundAccurate estimation of heading date of paddy rice greatly helps the breeders to understand the adaptability of different crop varieties in a given location. The heading date also plays a vital role in determining grain yield for research experiments. Visual examination of the crop is laborious and time consuming. Therefore, quick and precise estimation of heading date of paddy rice is highly essential.
resultsIn this work, we propose a simple pipeline to detect regions containing flowering panicles from ground level RGB images of paddy rice. Given a fixed region size for an image, the number of regions containing flowering panicles is directly proportional to the number of flowering panicles present. Consequently, we use the flowering panicle region counts to estimate the heading date of the crop. The method is based on image classification using Convolutional Neural Networks. We evaluated the performance of our algorithm on five time series image sequences of three different varieties of rice crops. When compared to the previous work on this dataset, the accuracy and general versatility of the method has been improved and heading date has been estimated with a mean absolute error of less than 1 day.
conclusionAn efficient heading date estimation method has been described for rice crops using time series RGB images of crop under natural field conditions. This study demonstrated that our method can reliably be used as a replacement of manual observation to detect the heading date of rice crops.
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