用AI自动评估农药喷洒效果，识别作物是否被喷及喷洒量。
Deep Learning for Precision Agriculture: Post-Spraying Evaluation and Deposition Estimation
- 基于EfficientNet-B0的全卷积网络，融合推理特征提升可解释性
- 对三种目标作物喷洒量估计误差平均仅156.8微升
- 适合农业自动化、智能植保系统研发人员使用
精准喷洒评估主要依赖喷后图像的自动化分析。本文提出一种可解释人工智能（XAI）计算机视觉流程，无需传统农学方法即可评估精准喷洒系统效果。该系统能语义分割生菜、小藜和草地早熟禾等潜在目标，并准确判断目标是否被喷洒。此外，通过领域特定的弱监督沉积估算任务，实现每类作物喷洒沉积量（单位：μL）的量化评估。类级别覆盖率分析有助于进一步理解精准喷洒系统的有效性。研究对比了AblationCAM与ScoreCAM两种类激活映射技术，验证其在任务中的有效性和可解释性。管道采用仅推理特征融合策略，增强可解释性并实现喷后评估自动化。实验表明，基于EfficientNet-B0主干的全卷积网络，在测试集上三类目标喷洒量估计的平均绝对误差为156.8 μL。本文构建的数据集已公开于https://github.com/Harry-Rogers/PSIE。
原文摘要 · Abstract (English)
Precision spraying evaluation requires automation primarily in post-spraying imagery. In this paper we propose an eXplainable Artificial Intelligence (XAI) computer vision pipeline to evaluate a precision spraying system post-spraying without the need for traditional agricultural methods. The developed system can semantically segment potential targets such as lettuce, chickweed, and meadowgrass and correctly identify if targets have been sprayed. Furthermore, this pipeline evaluates using a domain-specific Weakly Supervised Deposition Estimation task, allowing for class-specific quantification of spray deposit weights in μL. Estimation of coverage rates of spray deposition in a class-wise manner allows for further understanding of effectiveness of precision spraying systems. Our study evaluates different Class Activation Mapping techniques, namely AblationCAM and ScoreCAM, to determine which is more effective and interpretable for these tasks. In the pipeline, inference-only feature fusion is used to allow for further interpretability and to enable the automation of precision spraying evaluation post-spray. Our findings indicate that a Fully Convolutional Network with an EfficientNet-B0 backbone and inference-only feature fusion achieves an average absolute difference in deposition values of 156.8 μL across three classes in our test set. The dataset curated in this paper is publicly available at https://github.com/Harry-Rogers/PSIE
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