Detection and classification of wilting status in leaf images based on VGG16 with EfficientNet V3 algorithm

Qixiang Li, Yiming Ma, Ziyang Luo, Ying Tian
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Abstract

The aim of this paper is to explore the importance of leaf wilting status detection and classification in agriculture to meet the demand for monitoring and diagnosing plant growth conditions. By comparing the performance of the traditional VGG16 image classification algorithm and the popular EfficientNet V3 algorithm in leaf image wilting status detection and classification, it is found that EfficientNet V3 has faster convergence speed and higher accuracy. As the model training process proceeds, both algorithms show a trend of gradual convergence of Loss and Accuracy and increasing accuracy. The best training results show that VGG16 reaches a minimum loss of 0.288 and a maximum accuracy of 96% at the 19th epoch, while EfficientNet V3 reaches a minimum loss of 0.331 and a maximum accuracy of 97.5% at the 20th epoch. These findings reveal that EfficientNet V3 has a better performance in leaf wilting status detection, which provides a more accurate and efficient means of plant health monitoring for agricultural production and is of great research significance.
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基于 VGG16 和 EfficientNet V3 算法的叶片图像枯萎状态检测与分类
本文旨在探讨叶片萎蔫状态检测和分类在农业中的重要性,以满足监测和诊断植物生长状况的需求。通过比较传统的 VGG16 图像分类算法和流行的 EfficientNet V3 算法在叶片图像枯萎状态检测和分类中的性能,发现 EfficientNet V3 具有更快的收敛速度和更高的准确率。随着模型训练过程的进行,两种算法都呈现出损失率和准确率逐渐收敛、准确率不断提高的趋势。最佳训练结果显示,VGG16 在第 19 个 epoch 时达到了 0.288 的最小损失和 96% 的最高准确率,而 EfficientNet V3 在第 20 个 epoch 时达到了 0.331 的最小损失和 97.5% 的最高准确率。这些研究结果表明,EfficientNet V3 在叶片萎蔫状态检测方面具有更好的性能,为农业生产提供了更准确、更高效的植物健康监测手段,具有重要的研究意义。
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