Green citrus detection using fast Fourier transform (FFT) leakage

Detection of immature green citrus fruit is important during the early life cycle of citrus fruit. It allows growers to manage citrus groves more efficiently and maximize yields by identifying expected fruit yields well in advance before harvesting. It also helps the growers prepare harvesting equipment and pickers for the harvesting operation. A novel technique was developed for detecting immature green citrus fruit from an outdoor color image and counting number of fruits. This technique is unique in that it is the first known attempt towards exploring it on green citrus fruits. A set of 71 images containing immature green citrus fruit was acquired in an experimental citrus grove at the University of Florida, Gainesville, Florida, USA. An algorithm was developed using a set of 11 training images by calculating the fast Fourier transform leakage values for fruit and leaves. A threshold value was obtained by comparing the percent leakage of fruit and other objects. The algorithm was tested on a set of 60 validation images. The correct total fruit count for a validation set came out to be 120, whereas the actual number of fruit was 146. The overall correct detection rate was 82.2 %. The proposed algorithm can be further improved to help growers manage their grove more efficiently.     

冬枣果实物理参数与生物特性研究

为确定冬枣的机械选择性收获参数,试验测定不同成熟度果实的物理及生物特性参数,对其物理及生物特性参数之间的关系进行研究。结果表明:未熟期、白熟期和脆熟期的冬枣果实密度分别为902.15、911.68和947.06kg/m3,硬度分别为17.26、16.24和13.9kg/cm2,果实成熟度越高果实密度越大而果实硬度越小,脆熟期果实的密度及硬度与白熟期和未熟期果实的密度及硬度都存在显著性差异;未熟期、白熟期和脆熟期果实的树枝与果柄分离力都大于果实与果柄分离力,在机械振动收获时,果实脱落发生在果实与果柄连接处,分离力都随着成熟度的增加而减小,白熟期和脆熟期果实的果实与果柄分离力存在显著性差异,有望实现选择性收获;白熟期和脆熟期果实的压缩曲线趋势相似,都没有明显的生物屈服点。白熟期果实的破裂力为145.77N显著大于脆熟期果实的破裂力128.95N,果实和脆熟果实的压缩弹性模量均值分别为2.09和1.89 MPa,二者无显著性差异。果实破裂前,果实所受压力与变形呈近似线性关系。     

Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform

The mechanisation and automation of citrus harvesting is considered to be one of the best options to reduce production costs. Computer vision technology has been shown to be a useful tool for fresh fruit and vegetable inspection, and is currently used in post-harvest fruit and vegetable automated grading systems in packing houses. Although computer vision technology has been used in some harvesting robots, it is not commonly utilised in fruit grading during harvesting due to the difficulties involved in adapting it to field conditions. Carrying out fruit inspection before arrival at the packing lines could offer many advantages, such as having an accurate fruit assessment in order to decide among different fruit treatments or savings in the cost of transport and marketing non-commercial fruit. This work presents a computer vision system, mounted on a mobile platform where workers place the harvested fruits, that was specially designed for sorting fruit in the field. Due to the specific field conditions, an efficient and robust lighting system, very low-power image acquisition and processing hardware, and a reduced inspection chamber had to be developed. The equipment is capable of analysing fruit colour and size at a speed of eight fruits per second. The algorithms developed achieved prediction accuracy with an R² coefficient of 0.993 for size estimation and an R² coefficient of 0.918 for the colour index.     

尾张温柑挂树保鲜试验初报

于11月中旬,用200 ppm 2,4-D和150 ppm多菌灵处理果实,挂树保鲜120 d后,稳果率达100%,好果率达99%,果实平均增重4.63%,可溶性糖含量提高2.4%,净增利润1600元/t,但次年减产5.8%     

两种施肥模式下柑橘树体氮素吸收与运转机制的研究

以石棉县8年生黄果柑为材料,通过关键物候期（萌芽期、夏梢旺长期、果实迅速膨大期、转色期）施肥和常规施肥（谢花期、夏梢旺长期）两种模式施入15N\|尿素和普通尿素,测定夏梢旺长期、果实迅速膨大期以及转色期果实和叶片中的Ndff值以及成熟期树体不同器官的Ndff值、15N分配率以及15N利用率的差异情况。结果表明：在果实迅速膨大期、转色期以及成熟期3个关键时期,关键物候期施肥模式下柑橘果实的Ndff值显著大于常规模式下果实的Ndff值,两种模式下叶片的Ndff值在果实转色期均显著低于其他三个物候期,分别为2.04（关键物候期施肥）和2.01（常规施肥）;成熟期两种施肥模式均表现为果实中15N分配率最高,其中关键物候期施肥模式果实的15N分配率为50.2%,显著高于常规施肥模式的45.7%,关键物候期施肥模式下柑橘树体的15N利用率为35.7%,显著高于常规施肥的27.8%,表明关键物候期施肥能够使柑橘树体更充分地吸收、利用氮素。     

基于咬合型末端执行器的柑橘采摘机器人采摘姿态研究

针对采摘机器人视觉系统在复杂自然环境中无法准确提供柑橘果实生长姿态,进而导致采摘成功率下降的问题,基于柑橘采摘机器人咬合型末端执行器提出了一种最佳采摘姿态确定方法。该方法依据末端执行器构型参数,建立其采摘姿态对果实中心位置影响的性能评价函数,并使用该函数计算得到执行器最佳采摘姿态。通过搭建采摘实验平台和设计采摘实验,对计算出的最佳采摘姿态进行验证。实验结果表明,与一般的水平采摘姿态相比,采用最佳采摘姿态评价方法优化后的采摘姿态,在进行柑橘采摘时采摘成功率提高26.32%。     

Green citrus detection using ‘eigenfruit’, color and circular Gabor texture features under natural outdoor conditions

A machine vision algorithm was developed to detect and count immature green citrus fruits in natural canopies using color images. A total of 96 images were acquired in October 2010 from an experimental citrus grove in the University of Florida, Gainesville, Florida. Thirty-two of the total 96 images were selected randomly and used for training the algorithm, and 64 images were used for validation. Color, circular Gabor texture analysis and a novel ‘eigenfruit’ approach (inspired by the ‘eigenface’ face detection and recognition method) were used for green citrus detection. A shifting sub-window at three different scales was used to scan the entire image for finding the green fruits. Each sub-window was classified three times by eigenfruit approach using intensity component, eigenfruit approach using saturation component, and circular Gabor texture. Majority voting was performed to determine the results of the sub-window classifiers. Blob analysis was performed to merge multiple detections for the same fruit. For the validation set, 75.3% of the actual fruits were successfully detected using the proposed algorithm.     

Passion fruit detection and counting based on multiple scale faster R-CNN using RGB-D images

The accurate and reliable fruit detection in orchards is one of the most crucial tasks for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. However, detecting and counting small fruit is a very challenging task under variable lighting conditions, low-resolutions and heavy occlusion by neighboring fruits or foliage. To robustly detect small fruits, an improved method is proposed based on multiple scale faster region-based convolutional neural networks (MS-FRCNN) approach using the color and depth images acquired with an RGB-D camera. The architecture of MS-FRCNN is improved to detect lower-level features by incorporating feature maps from shallower convolution feature maps for regions of interest (ROI) pooling. The detection framework consists of three phases. Firstly, multiple scale feature extractors are used to extract low and high features from RGB and depth images respectively. Then, RGB-detector and depth-detector are trained separately using MS-FRCNN. Finally, late fusion methods are explored for combining the RGB and depth detector. The detection framework was demonstrated and evaluated on two datasets that include passion fruit images under variable illumination conditions and occlusion. Compared with the faster R-CNN detector of RGB-D images, the recall, the precision and F1-score of MS-FRCNN method increased from 0.922 to 0.962, 0.850 to 0.931 and 0.885 to 0.946, respectively. Furthermore, the MS-FRCNN method effectively improves small passion fruit detection by achieving 0.909 of the F1 score. It is concluded that the detector based on MS-FRCNN can be applied practically in the actual orchard environment.

     

板栗树振动响应特性与落果情况试验研究

为解决板栗人工采收效率低、成本高的问题,对板栗机械振动采收装备的设计提供参数支持.针对3棵板栗树,通过单偏心式振动电机激振树干对板栗树各分枝上加速度响应以及落果情况进行了试验研究.结果表明:3棵树树干和各果枝测点处的合加速度均与激振频率呈二次曲线增长关系,激振频率的增加有利于板栗树各果枝振动响应的增强;但受距激励点的距...     

基于三维重建的柑橘采摘机械臂避障研究与实验

利用机器人采摘柑橘果实需要解决机械臂运动过程中对障碍物的感知与避障问题。根据枝干的特征对枝干进行分段标记,使用深度学习Mask R-CNN神经网络进行训练、识别,然后与Kinect v2相机得到枝干障碍物关键点的三维信息进行重建。应用快速扩展随机树（rapidly-exploring random trees,RRT）的改进算法进行机械臂的避障运动规划。搭建了仿真及控制平台,并在实验室环境下通过课题组自行研制的柑橘收获机器人进行了验证,结果表明,样机避障成功率为90.7%,平均规划时间为1.5 s。上述结果为进一步进行实际环境采摘奠定了基础。     

Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach

One of the main problems in the post-harvest processing of citrus is the detection of visual defects in order to classify the fruit depending on their appearance. Species and cultivars of citrus present a high rate of unpredictability in texture and colour that makes it difficult to develop a general, unsupervised method able of perform this task. In this paper we study the use of a general approach that was originally developed for the detection of defects in random colour textures. It is based on a Multivariate Image Analysis strategy and uses Principal Component Analysis to extract a reference eigenspace from a matrix built by unfolding colour and spatial data from samples of defect-free peel. Test images are also unfolded and projected onto the reference eigenspace and the result is a score matrix which is used to compute defective maps based on the T² statistic. In addition, a multiresolution scheme is introduced in the original method to speed up the process. Unlike the techniques commonly used for the detection of defects in fruits, this is an unsupervised method that only needs a few samples to be trained. It is also a simple approach that is suitable for real-time compliance. Experimental work was performed on 120 samples of oranges and mandarins from four different cultivars: Clemenules, Marisol, Fortune, and Valencia. The success ratio for the detection of individual defects was 91.5%, while the classification ratio of damaged/sound samples was 94.2%. These results show that the studied method can be suitable for the task of citrus inspection.     

自然环境下果实作业机器人幼果期苹果侦测方法

首先,采用自适应G-B色差法对初始图像计算,获得色差灰度图,使用迭代阈值分割法提取果实兴趣区;其次,对经形态学处理后的兴趣区图像进行Blob分析,计算每个Blob的离心率和像素面积,去除明显偏离果实形状特点的Blob;最后,应用改进圆形Hough变换算法检测潜在类圆形果实目标,最终采用融合方向梯度直方图特征和网格搜索优化支持向量机的判别模型进一步去除虚假果实目标,提升苹果目标的侦测精确度。试验结果显示,该方法对果园自然环境下幼小青苹果的侦测正确率为88.51%,漏报率和误报率分别为11.49%和4.84%,算法模型综合性能指标为90.29%,表明该方法对幼果期苹果目标具有较强的侦测能力和较好的鲁棒性,该结果为果实作业机器人幼果期的自动化果实侦测提供参考。     

泰顺县野生果树资源

本文报道了浙江省泰顺县野生果树资源植物186种（包括种下分类等级）,隶属于29科46属,其中包括可直接作果品食用、加工成各种果品制成品食用和可作栽培果树育种材料3大类型。文中按果树学的分类方法,将它们分为仁果类（17种）、核果类（32种）、坚果类（20种）、浆果类（94种）、柑果类（3种）、聚复果类（10种）和柿枣类及其他（10种）7类。每类分别以种为单位作了描述,内容包括中名、学名、科名、生境、果熟期和利用价值6部分。文末对全县野生果树资源的开发利用提出了建议。     

基于双目立体视觉的重叠柑橘空间定位

果实的精准识别和定位是智能采摘面临的难题之一。基于双目立体视觉,提出了一种针对户外重叠柑橘的三维空间定位方法。首先,从双目左右图像中提取重叠柑橘果实轮廓并进行高斯平滑,通过曲率分析,找出异常的轮廓像素点;其次,依次连接相邻两个异常像素点,分析该线段上的像素点到柑橘轮廓的距离,在相邻两正常线段的交点处完成重叠柑橘轮廓分割,并通过寻找异常线段剔除对应的非柑橘轮廓像素点;再者,采用最小二乘椭圆拟合方法重建柑橘目标轮廓,并获取柑橘的中心;最后,根据双目极线约束和图像相似度,对重叠柑橘中心点进行匹配,并基于视差原理计算柑橘中心的深度值及三维空间坐标,确定重叠柑橘的遮挡关系。户外实验结果表明,所提出的方法定位误差为6.38 mm,满足柑橘采摘机器人户外采摘作业的定位精度要求。     

基于双目立体视觉的重叠柑橘空间定位

果实的精准识别和定位是智能采摘面临的难题之一。基于双目立体视觉,提出了一种针对户外重叠柑橘的三维空间定位方法。首先,从双目左右图像中提取重叠柑橘果实轮廓并进行高斯平滑,通过曲率分析,找出异常的轮廓像素点;其次,依次连接相邻两个异常像素点,分析该线段上的像素点到柑橘轮廓的距离,在相邻两正常线段的交点处完成重叠柑橘轮廓分割,并通过寻找异常线段剔除对应的非柑橘轮廓像素点;再者,采用最小二乘椭圆拟合方法重建柑橘目标轮廓,并获取柑橘的中心;最后,根据双目极线约束和图像相似度,对重叠柑橘中心点进行匹配,并基于视差原理计算柑橘中心的深度值及三维空间坐标,确定重叠柑橘的遮挡关系。户外实验结果表明,所提出的方法定位误差为6.38 mm,满足柑橘采摘机器人户外采摘作业的定位精度要求。     

手自一体式山核桃采摘机的设计与试验

为提高山核桃采摘效率,降低采摘成本,针对目前我国山核桃高空作业机械化程度低等特点,设计并研制了一款手自一体式山核桃采摘机。文章阐述了该机关键部件的设计,并对偏心轮机构进行数学建模与分析计算。应用ANSYS对果树进行自由模态响应分析,初步确定山核桃树采摘的频率范围为7~20 Hz。根据山核桃采摘试验,结果表明:振动频率对果树的采摘率具有显著影响(P=0.05),果实采摘率随振动频率的增大而增大,当振动频率为22 Hz时,采摘率为95.1%;为了提高采摘率且尽可能减小芽枝和果树的损伤,建议控制采收频率为16~18 Hz,此时果实的平均采摘率为83.9%~88.0%。未采摘的果实通过人工或机械二次采摘。     

Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images

A fast normalized cross correlation (FNCC) based machine vision algorithm was proposed in this study to develop a method for detecting and counting immature green citrus fruit using outdoor colour images toward the development of an early yield mapping system. As a template matching method, FNCC was used to detect potential fruit areas in the image, which was the very basis for subsequent false positive removal. Multiple features, including colour, shape and texture features, were combined in this algorithm to remove false positives. Circular Hough transform (CHT) was used to detect circles from images after background removal based on colour components. After building disks centred in centroids resulted from both FNCC and CHT, the detection results were merged based on the size and Euclidian distance of the intersection areas of the disks from these two methods. Finally, the number of fruit was determined after false positive removal using texture features. For a validation dataset of 59 images, 84.4 % of the fruits were successfully detected, which indicated the potential of the proposed method toward the development of an early yield mapping system.     

柑橘大实蝇对柑橘品种的选择及复杂环境的响应

2013年在麻阳县的混栽橘园调查了不同柑橘品种的柑橘大实蝇为害情况,同时在永顺县的橘园研究了柑橘大实蝇为害与周边环境的关系.结果表明,柑橘大实蝇对不同柑橘品种的产卵喜好程度为脐橙＞蜜橘＞冰糖橙＞椪柑,对脐橙的最早为害时间为6月19日左右,温州蜜柑和冰糖橙为6月25日左右,椪柑为7月19日左右;冰糖橙和椪柑的假产卵果比例较大,其果实硬度稍大于其他品种;周边有高大松树和其他灌木的橘园虫果率发生严重,而离高大松树和其他灌木距离超过100m的橘园虫果率较轻.     

篱剪对柑桔光分布与光合及结实的影响

研究了篱剪对密植郁闭桔园的作用，结果表明，通过篱剪①可以明显地改善树体的光环境，使树冠基部的光照强度平均比对照提高了１倍多；②使叶片的潜在光合能力得到发挥，改善了群体叶的光合生产力；③经过４年的积累统计，果实产量提高了３３％，并且显著增加了果实的糖、Ｖ＿ｃ和固形物含量，对促进果实膨大也有一定的作用。     

Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network

Detection of immature peach fruits would help growers to create yield maps which are very useful tools for adjusting management practices during the fruit maturing stages. Machine vision algorithms were developed to detect and count immature peach fruit in natural canopies using colour images. This study was the first effort to detect immature peach fruit in natural environment to the authors’ knowledge. Captured images had various illumination conditions due to both direct sunlight and diffusive light conditions that make the fruit detection task more difficult. A training set and a validation set were used to develop and to test the algorithms. Different image scanning methods including finding potential fruit regions were developed and used to parse fruit objects in the natural canopy image. Circular Gabor texture analysis and ‘eigenfruit’ approach (inspired by the ‘eigenface’ face detection and recognition method) were used for feature extraction. Statistical classifiers, a neural network and a support vector machine classifier were built and used for detecting peach fruit. A blob analysis was performed to merge multiple detections for the same peach fruit. Performance of the classifiers and image scanning methods were introduced and evaluated. Using the proposed algorithms, 84.6, 77.9 and 71.2 % of the actual fruits were successfully detected using three different image scanning methods for the validation set.