基于Faster R-CNN的作物生物密度智能识别方法

An intelligent recognition method for crop density based on Faster R-CNN

摘要:

准确获取大田作物数量和密度不仅是水肥管理按需投入的关键，也是保障作物产量和品质的关键。无人机(unmanned aerial vehicle, UAV)航拍可以快速且大面积地获取大田作物的分布图像信息，但是单一类型密集目标的准确识别对于大多数识别算法来说都是一个巨大的挑战。本研究以香蕉苗为例，通过无人机高空航拍香蕉园的图像，研究密集目标高效识别方法。本研究提出了一种“裁-识-拼”的策略，构建了一个基于改进的Faster R-CNN算法的计数方法。该方法先将包含高密集目标的图像按不同尺寸(模拟不同飞行高度)裁剪成大量图像瓦片，并采用对比度限制自适应直方图均衡化(contrast limited adaptive histogram equalization, CLAHE)算法提高图像质量，构建了包含36 000张图像瓦片的香蕉苗数据集；然后采用经过参数优化的Faster R-CNN网络训练香蕉苗识别模型；最后将识别结果进行反拼接，并设计了一种边界去重算法，对最终的计数结果进行校正，以减少图像裁剪引起的香蕉苗重复识别。结果表明，经过参数优化的Faster R-CNN对不同尺寸的香蕉图像数据集的识别精度最高达到了0.99；去重算法可以将针对航拍原始图像的平均计数误差从1.60%降低到0.60%，香蕉苗的平均计数准确率达到99.4%。本研究提出的方法有效解决了高分辨率航拍图像中密集小目标识别难题，为精准农业中的作物密度智能监测提供了高效可靠的技术支撑。

Abstract:

Accurately obtaining the crop quantity and density is not only crucial for the demand-based input of water and fertilizer in the field but also vital for ensuring the yield and quality of crops. Aerial photography by unmanned aerial vehicles (UAVs) can quickly acquire the distribution image information of crops over a large area. However, the accurate recognition of a single type of dense targets is a huge challenge for most recognition algorithms. Taking banana seedlings as an example in this study, we captured the images of banana plantations by UAVs from high altitudes to explore an efficient recognition method for dense targets. We proposed a strategy of "cut-recognition-stitch" and constructed a counting method based on the improved Faster R-CNN algorithm. First, the images containing highly dense targets were cropped into a large number of image tiles according to different sizes (simulating different flight altitudes), and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was adopted to improve the image quality. A banana seedling dataset containing 36 000 image tiles was constructed. Then, the Faster R-CNN network with optimized parameters was used to train the banana seedling recognition model. Finally, the recognition results were reversely stitched together, and a boundary deduplication algorithm was designed to correct the final counting results to reduce the repeated recognition caused by image cropping. The results show that the recognition accuracy of the Faster R-CNN with optimized parameters for banana image datasets of different sizes can reach up to 0.99 at most. The deduplication algorithm can reduce the average counting error for the original aerial images from 1.60% to 0.60%, and the average counting accuracy of banana seedlings reaches 99.4%. The proposed method effectively addresses the challenge of recognizing dense small objects in high-resolution aerial images, providing an efficient and reliable technical solution for intelligent crop density monitoring in precision agriculture.