用深度学习自动识别油棕果成熟度，提升采收效率
Deep Convolutional Neural Networks for Palm Fruit Maturity Classification
- 基于CNN模型分析油棕果图像，分五级判断成熟度
- 测试准确率超85%，优于基础模型
- 适合农业智能化、智能采摘系统研发者
为最大化棕榈油产量与品质，需在最佳成熟阶段采摘油棕果。本研究旨在开发一种自动化计算机视觉系统，精确将油棕果图像分类为五个成熟等级。采用深度卷积神经网络（CNN）进行成熟度分类，以浅层CNN为基线模型，并对预训练的ResNet50和InceptionV3架构应用迁移学习与微调。实验使用公开数据集，包含超过8,000张图像，存在显著变化，按80%训练、20%测试划分。所提深度CNN模型在测试中达到超过85%的准确率，验证了深度学习在自动化油棕果成熟度评估中的潜力，有助于优化采收决策，提升棕榈油生产效率。
原文摘要 · Abstract (English)
To maximize palm oil yield and quality, it is essential to harvest palm fruit at the optimal maturity stage. This project aims to develop an automated computer vision system capable of accurately classifying palm fruit images into five ripeness levels. We employ deep Convolutional Neural Networks (CNNs) to classify palm fruit images based on their maturity stage. A shallow CNN serves as the baseline model, while transfer learning and fine-tuning are applied to pre-trained ResNet50 and InceptionV3 architectures. The study utilizes a publicly available dataset of over 8,000 images with significant variations, which is split into 80\% for training and 20\% for testing. The proposed deep CNN models achieve test accuracies exceeding 85\% in classifying palm fruit maturity stages. This research highlights the potential of deep learning for automating palm fruit ripeness assessment, which can contribute to optimizing harvesting decisions and improving palm oil production efficiency.
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