不同光照与水分条件下2种白三叶生长特性比较

为探究不同光照和水分条件对不同品种白三叶（Trifolium repens L.）生长特性的影响,本研究以TKPR和TNSP 2个白三叶品种为试验对象,通过温室控制设定了3个光照梯度和3个水分梯度共9个组合处理,分析了不同处理下白三叶形态、生物量积累、生物量分配比及其可塑性指标的响应特征。结果表明：品种TKPR各指标受光照强度影响较大,品种TNSP受水分强度、光照与水分交互作用的影响较大;品种TNSP的相对生长速率大于TKPR;在适宜的环境下品种TNSP的生物量积累高于品种TKPR;光照与水分的交互作用对品种TKPR的开花繁殖策略影响较大;在不利的环境下品种TKPR适应可塑性较强,TNSP较弱。本研究结果为培育白三叶的新品种和高效生产栽培管理等方面提供了一定的参考依据。     

基于YOLOv4和自适应锚框调整的谷穗检测方法

Foxtail millet ear detection and counting are essential for the estimation of foxtail millet production and breeding. However, traditional foxtail millet ear counting approaches based on manual statistics are usually time-consuming and labor-intensive. In order to count the foxtail millet ears accurately and efficiently, an adaptive anchor box adjustment foxtail millet ear detection method was proposed in this research. Ear detection dataset was firstly established, including 784 images and 10,000 ear samples. Furthermore, a novel foxtail millet ear detection approach based on YOLOv4 (You Only Look Once) was developed to quickly and accurately detect the ear of foxtail millet in the specific box. For verifying the effectiveness of the proposed approach, several criteria, including the mean average Precision, F1-score,Recall and mAP were employed. Moreover, ablation studies were designed to validate the effectiveness of the proposed method, including (1) evaluating the performance of the proposed model through comparing with other models (YOLOv2, YOLOv3 and Faster-RCNN); (2) evaluating the model with different Intersection over Union (IOU) thresholds to achieve the optimal IOU thresholds; (3) evaluating the foxtail millet ear detection with or without anchor boxes adjustment to verify the effectiveness of the adjustment of anchor boxes;(4) evaluating the changing reasons of model criteria and (5) evaluating the foxtail millet ear detection with different input original image size respectively. Experimental results showed that YOLOv4 could obtain the superior ear detection performance. Specifically, mAP and F1-score of YOLOv4 achieved 78.99% and 83.00%, respectively. The Precision was 87% and the Recall was 79.00%, which was about 8% better than YOLOv2, YOLOv3 and Faster RCNN models, in terms of all criteria. Moreover, experimental results indicates that the proposed method is superior with promising accuracy and faster speed.     

自然场景下多目标苹果识别定位融合算法研究

自然场景下苹果采摘对目标的精准识别和三维定位是苹果智能采摘设备的关键技术。融合YOLOv3算法和双目视觉技术,通过YOLOv3算法对多种自然场景下的样本进行训练,构建识别模型,利用双目视觉获取苹果图像,运用YOLOv3模型得到图像中目标苹果的二维坐标,再利用双目视觉视差原理得到深度坐标信息,从而实现对目标苹果的三维空间定位。将该算法应用于不同自然场景下苹果的识别和定位,并进行识别效果和定位精度的评价。结果表明,在光照不均、果实上存在阴影并且存在相互遮挡的情况下,最小相对误差为0.193%,最大相对误差为3.670%;在夜晚光照不足且存在相互遮挡的情况下,最小相对误差为0.831%,最大相对误差为4.417%;有露水在苹果表面形成反射并且果实存在相互遮挡的情况下,最小相对误差为0.176%,最大相对误差为4.205%;在光线较弱、阴影小、存在遮挡时,最小相对误差为0.168%,最大相对为误差3.776%。研究中所运用的算法只需适量样本就可以满足不同场景下的识别和定位训练,在不同场景下的mAP(mean Average Precision)达96.60%。该算法具有较强的稳定性,能够识别重...