面向不丹资源受限温室环境的番茄生长阶段检测的轻量级物候感知YOLOv5框架
A Lightweight Phenology-Aware YOLOv5 Framework for Tomato Growth Stage Detection in Resource-Constrained Bhutanese Greenhouse Environments
AI总结：
针对不丹资源受限温室环境，提出轻量级物候感知的Pheno-Lite+ECA架构，构建含2464张图像的番茄数据集，实现92.6% mAP@50，可用于当地番茄生长阶段检测
AI中文摘要：
准确检测番茄生长阶段对于特定阶段的温室管理和精准农业至关重要。在不丹，温室种植受海拔差异、大昼夜温差、漫射光照、自动化程度有限以及本地标注数据集稀缺的影响，限制了传统深度学习模型的适用性。本研究提出了Pheno-Lite + Efficient Channel Attention（ECA），这是一种源自Ultralytics YOLOv5的轻量级、物候感知目标检测架构，用于番茄生长阶段识别。从在不丹本地收集的温室图像和公开的番茄图像中构建了包含2464张标注图像的平衡数据集，增强操作旨在模拟当地温室条件。该数据集包含营养生长（820个）、开花（824个）、结果（820个）和背景（26个）样本。所提出的架构引入了两个定制的骨干模块：C3 PhenoLite，通过深度残差细化增强空间和纹理特征提取；以及C3 ECA，通过高效通道注意力强化通道间特征交互。该模型在640×640分辨率下实现了90.6%的精度、88.8%的召回率和92.6%的mAP@50，拥有400万个参数和10.9 GFLOPs。这些结果证明了其在不丹用于实时、气候适应型温室部署的潜力。
英文摘要：
Accurate detection of tomato growth stages is essential for stage-specific greenhouse management and precision agriculture. In Bhutan, greenhouse cultivation is affected by altitude variability, large diurnal temperature fluctuations, diffuse illumination, limited automation, and a scarcity of locally annotated datasets, limiting the applicability of conventional deep learning models. This work proposes Pheno-Lite + Efficient Channel Attention (ECA), a lightweight, phenology-aware object detection architecture derived from Ultralytics YOLOv5 for tomato growth stage recognition. A balanced dataset of 2,464 annotated images was constructed from locally collected greenhouse images in Bhutan and publicly available tomato images, with augmentation designed to simulate local greenhouse conditions. The dataset includes vegetative (820), flowering (824), fruiting (820), and background (26) samples. The proposed architecture introduces two customized backbone modules: C3 PhenoLite, which enhances spatial and texture feature extraction using depthwise residual refinement, and C3 ECA, which strengthens inter-channel feature interactions through efficient channel attention. The proposed model achieves 90.6% precision, 88.8% recall, and 92.6% mAP@50, with 4.0 million parameters and 10.9 GFLOPs at 640 x 640 resolution. These results demonstrate its potential for real-time and climate-resilient greenhouse deployment in Bhutan.
