用图像分类神经网络在草中检测和覆盖范围的估计
Xiaojun Jin1,2, Kang Han2, Hua Zhao3
1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
Pest management science
|March 4, 2024
概括
这项研究表明,图像分类神经网络 (NN) 能够准确地检测杂草,并估计草皮草的覆盖面. ResNet成为精密除草剂应用中最有效,最准确的深度学习模型.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 植物科学 植物科学
背景情况:
- 精确的除草剂应用需要精确的杂草检测和覆盖率估计.
- 深度学习 (DL) 方法通常需要广泛的注释数据来进行像素级分析.
- 这项研究探讨了图像分类神经网络 (NN),用于在百慕大草草草中评估杂草.
研究的目的:
- 评估图像分类NN在百慕大草草草的杂草检测和覆盖率估计方面的有效性.
- 为了比较不同NN架构 (DenseNet,GoogLeNet,ResNet) 的性能.
- 确定用于杂草管理的最有效和最准确的DL模型.
主要方法:
- 使用图像分类神经网络 (NN),包括DenseNet,GoogLeNet和ResNet.
- 进行了k倍交叉验证以评估模型准确性和F1分数.
- 评估了在验证和测试数据集上的杂草检测和覆盖率估计性能.
主要成果:
- 所有评估的NN都获得了高准确度和F1评分 (≥0.971) 来检测杂草.
- 在DenseNet中,杂草覆盖率估计的最高准确度 (0.977) 和F1得分 (0.996) 是最高的.
- ResNet的推断速度与GoogleLeNet相比,在杂草检测和覆盖率估计方面具有更高的效率和准确性.
结论:
- 发达的NN可以有效地检测杂草,并估计百慕大草草的覆盖面.
- 这些发现支持可变速率除草剂应用和自主特定地点喷雾系统.
- 图像分类NN为杂草管理提供了像素级DL方法的可行替代方案.
相关概念视频
02:16Light Acquisition
8.5K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.5K
02:35Key Elements for Plant Nutrition
18.7K
Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
18.7K