使用1D卷积神经网络进行持续增长监测和预测,使用视觉转换器生成的数据进行视觉转换
Woo-Joo Choi1, Se-Hun Jang1, Taewon Moon2
1Division of Animal, Horticultural and Food Sciences, Chungbuk National University, Cheongju 28644, Republic of Korea.
Plants (Basel, Switzerland)
|November 9, 2024
概括
本研究介绍了一种非破坏性方法,使用RGB图像和计算机视觉来预测作物生长. 这些模型准确地估计了生菜的生长,使实时监测成为增强作物管理的必要条件.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 传统的作物生长监测涉及破坏性采样,导致数据不连续.
- 实时,非破坏性监测对于动态反控制和立即作物生长评估至关重要.
- RGB图像提供丰富的,非破坏性的关于作物发展阶段的数据.
研究的目的:
- 开发和验证一种使用低成本RGB图像和计算机视觉的非破坏性作物生长预测方法.
- 为了比较图像对增长和增长模拟模型的性能,用于预测作物生物质和叶面积.
- 展示深度学习在实时作物监测和环境反控制方面的潜力.
主要方法:
- 利用低成本的RGB图像作为计算机视觉模型的输入.
- 开发并比较了两个方法:图像增长模型和增长模拟模型.
- 评估了视觉变压器 (ViT) 和1D卷积神经网络 (1D ConvNet) 的性能,用于预测菜的新鲜重量,干燥重量和叶面积.
主要成果:
- 视觉变压器 (ViT) 的R2值为0.89 (新鲜重量),0.93 (干重量) 和0.78 (叶面积).
- 一维卷积神经网络 (1D ConvNet) 显示出卓越的性能,R2值为0.96 (新鲜重量),0.94 (干重量) 和0.95 (叶面积).
- 高精度证实RGB图像和深层神经网络可以有效地解释作物与环境的相互作用.
结论:
- 使用RGB图像和深度学习进行非破坏性增长预测是可行的和准确的.
- 1D ConvNet模型在预测菜的关键生长参数方面表现出色.
- 这种方法使种植者能够提高资源利用效率,并通过实时监测和反控制获得高质量的作物.
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