UAV time-series imagery with novel machine learning to estimate heading dates of rice accessions for breeding

Lyu, Meiling; Lü, Xin; Shen, Yuan; Tan, Yuanyuan; Wan, Lijuan; Shu, Qingyao; He, Yuhong; He, Yong; Cen, Haiyan

doi:10.1016/j.agrformet.2023.109646

Cited by 6 publications

(4 citation statements)

References 59 publications

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“…In addition, the time complexity in the position updating process of the agent population is O(n × m). The time complexity of updating the position of the population through 'imitation learning' and 'perception learning' is O (1). The time complexity of creating the elite candidate pool and selecting the candidate population is O (1).…”

Section: Time Complexity Analysismentioning

confidence: 99%

“…The time complexity of updating the position of the population through 'imitation learning' and 'perception learning' is O (1). The time complexity of creating the elite candidate pool and selecting the candidate population is O (1). According to the algorithmic steps in the literature [19], the time complexity of the new and improved LSCSO algorithm is computed in the form of O(n…”

Section: Time Complexity Analysismentioning

confidence: 99%

“…In recent years unmanned aerial vehicle (UAV) has been widely used in agriculture [1], environmental monitoring [2], construction [3], logistics and transportation [4], disaster response [5], scientific exploration [6], entertainment and media [7], and many other fields. Particularly noteworthy is that UAV path planning, as an important research direction, has a direct impact on the practical effects and performance optimization of UAVs in several fields.…”

Section: Introductionmentioning

confidence: 99%

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A novel unmanned aerial vehicle path planning approach: sand cat optimization algorithm incorporating learned behaviour

Hu,

2024

Meas. Sci. Technol.

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Unmanned aerial vehicle(UAV) path planning plays an important role in UAV flight, and an effective algorithm is needed to realize UAV path planning. The sand cat algorithm is characterized by simple parameter setting and easy implementation. However, the convergence speed is slow, easy to fall into the local optimum. In order to solve these problems, a novel sand cat algorithm incorporating learning behaviors (LSCSO) is proposed. LSCSO is inspired by the life habits and learning ability of sand cats and incorporates a new position update strategy into the basic Sand Cat Optimization Algorithm, which maintains the diversity of the population and improves the convergence ability during the optimization process. Finally, LSCSO is applied to the challenging UAV 3D path planning with cubic B-spline interpolation to generate a smooth path, and the proposed algorithm is compared with a variety of other competing algorithms. The experimental results show that LSCSO has excellent optimization-seeking ability and plans a safe and feasible path with minimal cost consideration among all the compared algorithms.

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Section: Time Complexity Analysismentioning

confidence: 99%

Section: Time Complexity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel unmanned aerial vehicle path planning approach: sand cat optimization algorithm incorporating learned behaviour

Hu,

2024

Meas. Sci. Technol.

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“…A lightweight model called TinyCCNet for rice panicle segmentation in UAV images is developed, showing potential for agricultural UAVs with limited computing resources ( Ramachandran and K.S., 2023 ). The Res2Net model has been used to classify growth stages and partial least squares regression to estimate heading date from UAV time series images, achieving high accuracy ( Lyu et al., 2023 ). Overall, these studies demonstrate deep learning and computer vision techniques enable accurate, automatic analysis of panicle development from both aerial and ground-based imagery.…”

Section: Introductionmentioning

confidence: 99%

High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits

Chen,

Lu,

Wang

et al. 2024

Front. Plant Sci.

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IntroductionRice (Oryza sativa) serves as a vital staple crop that feeds over half the world's population. Optimizing rice breeding for increasing grain yield is critical for global food security. Heading-date-related or Flowering-time-related traits, is a key factor determining yield potential. However, traditional manual phenotyping methods for these traits are time-consuming and labor-intensive.MethodHere we show that aerial imagery from unmanned aerial vehicles (UAVs), when combined with deep learning-based panicle detection, enables high-throughput phenotyping of heading-date-related traits. We systematically evaluated various state-of-the-art object detectors on rice panicle counting and identified YOLOv8-X as the optimal detector.ResultsApplying YOLOv8-X to UAV time-series images of 294 rice recombinant inbred lines (RILs) allowed accurate quantification of six heading-date-related traits. Utilizing these phenotypes, we identified quantitative trait loci (QTL), including verified loci and novel loci, associated with heading date.DiscussionOur optimized UAV phenotyping and computer vision pipeline may facilitate scalable molecular identification of heading-date-related genes and guide enhancements in rice yield and adaptation.

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Prior knowledge and active learning enable hybrid method for estimating leaf chlorophyll content from multi-scale canopy reflectance

Wan,

Liu,

et al. 2023

Computers and Electronics in Agriculture

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UAV time-series imagery with novel machine learning to estimate heading dates of rice accessions for breeding

Cited by 6 publications

References 59 publications

A novel unmanned aerial vehicle path planning approach: sand cat optimization algorithm incorporating learned behaviour

A novel unmanned aerial vehicle path planning approach: sand cat optimization algorithm incorporating learned behaviour

High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits

Prior knowledge and active learning enable hybrid method for estimating leaf chlorophyll content from multi-scale canopy reflectance

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