基于改进全卷积网络的黄瓜产量LAI估计
LAI Estimation of Cucumber Crop Based on Improved Fully Convolutional Network
摘要：
叶面积指数（LAI）是作物估产的重要农艺指标。它与植物生长状况、净同化率、植物光合作用、环境中的二氧化碳等有直接关系。如何准确、高效地测定叶面积指数是农作物估产的关键。人工测量耗费大量的人力物力。遥感技术不适用于近地面叶面积指数的测量。此外，基于传统数字图像处理的方法受环境噪声和图像曝光的影响较大。如今，深度学习在许多领域得到了广泛的应用。本文提出了一种改进的全卷积网络（FCN）用于叶面积指数（LAI）的测量。从我们的温室收集的82个黄瓜图像被标记以微调预训练模型。结果表明，改进的FCN模型在我们的数据集上表现良好。我们的方法的平均IoU可以达到0.908，这是11%，比传统的方法和4.7%，比基本的FCN模型。
英文摘要：
LAI (Leaf Area Index) is of great importance for crop yield estimation in agronomy. It is directly related to plant growth status, net assimilation rate, plant photosynthesis, and carbon dioxide in the environment. How to measure LAI accurately and efficiently is the key to the crop yield estimation problem. Manual measurement consumes a lot of human resources and material resources. Remote sensing technology is not suitable for near-Earth LAI measurement. Besides, methods based on traditional digital image processing are greatly affected by environmental noise and image exposure. Nowadays, deep learning is widely used in many fields. The improved FCN (Fully Convolutional Network) is proposed in our study for LAI measure task. Eighty-two cucumber images collected from our greenhouse are labeled to fine-tuning the pre-trained model. The result shows that the improved FCN model performs well on our dataset. Our method's mean IoU can reach 0.908, which is 11% better than conventional methods and 4.7% better than the basic FCN model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
