Lu Hao, Cao Zhiguo
Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China.
Front Plant Sci. 2020 Dec 7;11:541960. doi: 10.3389/fpls.2020.541960. eCollection 2020.
Plant counting runs through almost every stage of agricultural production from seed breeding, germination, cultivation, fertilization, pollination to yield estimation, and harvesting. With the prevalence of digital cameras, graphics processing units and deep learning-based computer vision technology, plant counting has gradually shifted from traditional manual observation to vision-based automated solutions. One of popular solutions is a state-of-the-art object detection technique called Faster R-CNN where plant counts can be estimated from the number of bounding boxes detected. It has become a standard configuration for many plant counting systems in plant phenotyping. Faster R-CNN, however, is expensive in computation, particularly when dealing with high-resolution images. Unfortunately high-resolution imagery is frequently used in modern plant phenotyping platforms such as unmanned aerial vehicles, engendering inefficient image analysis. Such inefficiency largely limits the throughput of a phenotyping system. The goal of this work hence is to provide an effective and efficient tool for high-throughput plant counting from high-resolution RGB imagery. In contrast to conventional object detection, we encourage another promising paradigm termed object counting where plant counts are directly regressed from images, without detecting bounding boxes. In this work, by profiling the computational bottleneck, we implement a fast version of a state-of-the-art plant counting model TasselNetV2 with several minor yet effective modifications. We also provide insights why these modifications make sense. This fast version, TasselNetV2+, runs an order of magnitude faster than TasselNetV2, achieving around 30 fps on image resolution of 1980 × 1080, while it still retains the same level of counting accuracy. We validate its effectiveness on three plant counting tasks, including wheat ears counting, maize tassels counting, and sorghum heads counting. To encourage the use of this tool, our implementation has been made available online at https://tinyurl.com/TasselNetV2plus.
植株计数贯穿于农业生产的几乎每个阶段，从种子培育、发芽、栽培、施肥、授粉到产量估算以及收获。随着数码相机、图形处理单元和基于深度学习的计算机视觉技术的普及，植株计数已逐渐从传统的人工观测转向基于视觉的自动化解决方案。一种流行的解决方案是一种名为Faster R-CNN的先进目标检测技术，通过检测到的边界框数量来估算植株数量。它已成为许多植物表型分析中植株计数系统的标准配置。然而，Faster R-CNN计算成本高昂，尤其是在处理高分辨率图像时。不幸的是，高分辨率图像在诸如无人机等现代植物表型分析平台中经常被使用，导致图像分析效率低下。这种低效率在很大程度上限制了表型分析系统的通量。因此，这项工作的目标是为从高分辨率RGB图像中进行高通量植株计数提供一种有效且高效的工具。与传统目标检测不同，我们倡导另一种有前景的范式——目标计数，即直接从图像中回归植株数量，而无需检测边界框。在这项工作中，通过分析计算瓶颈，我们对一种先进的植株计数模型TasselNetV2进行了一些微小但有效的修改，实现了其快速版本。我们还阐述了这些修改为何合理。这个快速版本TasselNetV2+的运行速度比TasselNetV2快一个数量级，在图像分辨率为1980×1080时达到约30帧每秒，同时仍保持相同水平的计数精度。我们在三个植株计数任务上验证了其有效性，包括麦穗计数、玉米雄穗计数和高粱穗计数。为鼓励使用此工具，我们的实现已在https://tinyurl.com/TasselNetV2plus上在线提供。
