Automatic acquisition of tomato canopies' phenotypic traits is essential for tomato varieties' selection and scientific cultivation. Due to the infinite growth characteristics of tomato, its organ development ...
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Automatic acquisition of tomato canopies' phenotypic traits is essential for tomato varieties' selection and scientific cultivation. Due to the infinite growth characteristics of tomato, its organ development is stochastic and the canopies' internal structure of is also complex. These make it challenging to obtain organs' detailed phenotypic traits. Thus, this work proposed a method for detecting tomato canopies' phenotypic traits based on improved skeleton extraction algorithm (ISEA). Firstly, after collecting tomato canopies' point cloud data from multiple perspectives, this work reconstructed its three-dimensional (3D) model accurately. Secondly, the least squares method was used to simplify the spatial contraction model of the Laplace Skeleton Extraction algorithm to obtain the tomatoes' skeleton point set. On this basic, Greedy algorithm was used to optimise the Edge Collapse algorithm to extract more accurate and reliable skeleton structure. Then, in conjunction with the canopy growth characteristics of the crop and the Intrinsic Shape Signatures (ISS) principle, the simplified skeleton structure was subjected to local principal component analysis (PCA), which achieved the separation of tomatoes' main stem and leaves. Finally, a modelling algorithm based on Delaunay triangulation was applied to construct the separated organs' model to calculate phenotypic traits such as stem diameter, leaf area index and average leaf inclination. The calculated results were also compared with the measured values at different growth stages for performance evaluation. The average precision, average recall, average accuracy and micro F1 score of ISEA were 0.9144, 0.7751, 0.7243 and 0.8306, respectively. The overall R2 between calculated values and measured values for stem diameter, leaf area index and average leaf inclination were 0.9638, 0.9067, and 0.9428, and the root mean square errors (RMSE) were 0.3922, 0.0029, and 0.0186, respectively. As a result, the proposed method can
This paper embarks on a meticulous comparative exploration of two venerable algorithms often invoked in multi-armed bandit problems: the Kullback-Leibler Upper Confidence Bound(KL-UCB) and the generic Upper Confidence...
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This paper embarks on a meticulous comparative exploration of two venerable algorithms often invoked in multi-armed bandit problems: the Kullback-Leibler Upper Confidence Bound(KL-UCB) and the generic Upper Confidence Bound(UCB) ***, a comprehensive discourse is presented, elucidating the definition, evolution, and realworld applications of both algorithms. The crux of the study then shifts to a side-by-side comparison, weighing the regret performance and time complexities when applied to a quintessential movie rating dataset. In the trenches of practical implementations, addressing multi-armed bandit problems invariably demands extensive training. Consequently, even seemingly minor variations in algorithmic complexity can usher in pronounced differences in computational durations and resource utilization. This inherent intricacy prompts introspection:Is the potency of a given algorithm in addressing diverse practical quandaries commensurate with its inherent complexity. By juxtaposing the KL-UCB and UCB algorithms, this study not only highlights their relative merits and demerits but also furnishes insights that could serve as catalysts for further refinement and optimization. The overarching aim is to cultivate an informed perspective, guiding practitioners in choosing or fine-tuning algorithms tailored to specific applications without incurring undue computational overheads.
A self-adaptive differential evolution with dynamic selecting mutation strategy (DSMSDE) is proposed to improve the performance of differential evolution algorithm by three improvements. Mutation strategies are dynami...
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ISBN:
(纸本)9781538606124
A self-adaptive differential evolution with dynamic selecting mutation strategy (DSMSDE) is proposed to improve the performance of differential evolution algorithm by three improvements. Mutation strategies are dynamically selected, and the successfully updated individuals are stored into the archive, which is beneficial for improving the convergence performance. A mechanism that is related to the best individual at the current population is employed to help the stagnation solutions to get rid of local minima. Self-adaptive parameters control is used to accelerate the convergence speed. DSMSDE is compared with the other state-of-the-art algorithms, and they are tested on nine benchmark functions. Experimental results show that DSMSDE has higher accuracy, faster speed and better reliability.
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