基于最小矩形法的柑桔横径实时检测方法研究

农产品的大小是农产品分级的重要特征之一．本文以柑桔为研究对象,对利用计算机图像处理技术检测柑桔横径的方法进行了探索．研究了柑桔图像的平滑和边缘提取方法,比较了利用Sobel算子和跟踪虫（tracking bug)法所得到的柑桔边缘,发现跟踪虫法寻找边界的速度比用Sobel算子快,且不需要进行细化处理．为了适应实际生产中柑桔方向的随机性和外形的不规则性的要求,使柑桔尺寸检测的方法有更好的适应性,设计了一种利用柑桔的最小外接矩形（MER）求最大横径的方法,并在柑桔的外形尺寸检测中进行了验证,实际最大横径Dr与预测最大横径的相关性为0．9982,利用此方法估测果径的最大误差在1．30mm以内,最大相对误差为2.64％,平均相对误差为1.35％,GB／T12947-91鲜柑桔分级标准中均以5mm为分等标准差,故MER法的精度能满足实际生产中柑桔分级精度的要求．本研究结果不仅为进一步研究开发基于图像处理技术的柑桔品质检测系统打下了基础,而且可用于对其他农产品进行检测．     

激光技术在农产品质量检测中的研究进展

近年来激光在农业领域得到广泛的应用和研究,其中的一个最新进展是将激光技术应用于农产品内部品质和安全性检测。本文介绍了农产品质量检测中的几种激光技术,包括应用激光的吸收与反射技术来检测农产品糖酸度、质地、PH值、成熟度、干物质等;应用激光诱导荧光技术来检测农产品的农药残留、叶绿素、成熟度;应用激光拉曼光谱技术来检测农产品水果损伤、农药残留。对农产品激光检测的未来发展趋势进行了探讨和展望。     

近红外光谱技术在水果成熟期预测中的应用(综述)

近红外光谱技术以快速、准确和多组分同步分析等优势,近年来在水果果实发育、成熟期预测和品质检测等方面应用广泛,并在果实品质无损检测分析技术研究方面取得重要进展。本文综述近红外光谱技术的基本原理和特点,分析近红外光谱技术在果实成熟期预测中的研究现状和存在问题,并提出今后研究方向。     

基于矩阵广义逆和奇异值分解的运动水果模糊图像恢复

针对利用机器视觉对进行水果分级时,由于水果运动所造成的模糊问题,提出了基于矩阵广义逆和奇异值分解的恢复方法,实验表明恢复的图像比较清晰,并且在保证实时的条件下可将水果大小检测的相对误差从4．17%减小为0．671%,相比于传统的恢复方法而言,提高了速度,消除了误差积累,为后续的边缘检测、形状分析、缺陷分类等打下了基础．     

基于Matlab语言的杂交水稻品种的颜色特征

本研究组建了基于机器视觉的杂交水稻种子检测硬件软件系统,并利用Matlab语言平台提取了杂交水稻种子图像的R、G、B、H、S、V六个颜色特征参数,研究了六个参数与水稻的品种相关性,实验证明通过颜色识别杂交水稻品种具有可行性,此结果为杂交水稻种子的基于机器视觉的识别方法提供了参考价值．     

柑桔黄龙病PCR快速检测技术的建立与应用

利用柑桔黄龙病病原亚洲种16S rDNA序列的特异引物,建立了依据16S rDNA基因序列扩增的有无检测柑桔黄龙病的PCR方法。该检测方法特异性强,重复性好,对大田中感病植株的检出率高,也适用于柑桔无病毒苗木的大量检测。     

红外相机监测目标物种的一种自动化检测方法：以绿尾虹雉为例

红外相机是目前在野生动物资源调查和监测中的一种重要手段和工具,但对其采集的海量影像数据的甄别和所拍获物种的鉴定工作费时费力。为解决红外相机影像数据量庞大、无法自动识别目标物种、人为检索繁琐、以及卷积神经网络方法的检测效率和鉴别正确率低等问题,本文对红外相机采集的11万余张图像进行筛选,以绿尾虹雉Lophophorus lhuysii为例,运用协同注意力机制,提出一种针对红外相机影像数据中目标物种的自动化检测方法。实验结果表明,该方法对绿尾虹雉图像与视频的识别准确度达到99.62%。本文提出的方法能够提高对检测目标物种的识别率,降低人力成本,有利于指导野生动物的监测和保护。     

基于颜色名和OpponentSIFT特征的鳞翅目昆虫图像识别

【目的】本研究旨在探索使用先进的计算机视觉技术实现对昆虫图像的自动分类方法。【方法】通过预处理对采集的昆虫标本图像去除背景,获得昆虫图像的前景蒙板,并由蒙板确定的轮廓计算出前景图像的最小包围盒,剪切出由最小包围盒确定的前景有效区域,然后对剪切得到的图像进行特征提取。首先提取颜色名特征,把原来的RGB（Red-Green-Blue）图像的像素值映射到11种颜色名空间,其值表示RGB值属于该颜色名的概率,每个颜色名平面划分成3×3像素大小的网格,用每格的概率均值作为网格中心点的描述子,最后用空阈金字塔直方图统计的方式形成颜色名视觉词袋特征;其次提取OpponentSIFT(Opponent Scale Invariant Feature Transform)特征,首先把RGB图像变换到对立色空间,对该空间每通道提取SIFT特征,最后用空域池化和直方图统计方法形成OpponentSIFT视觉词袋。将两种词袋特征串接后得到该昆虫图像的特征向量。使用昆虫图像样本训练集提取到的特征向量训练SVM（Support Vector Machine）分类器,使用这些训练得到的分类器即可实现对鳞翅目昆虫的分类识别。【结果】该方法在包含10种576个样本的昆虫图像数据库中进行了测试,取得了100%的识别正确率。【结论】试验结果证明基于颜色名和OpponentSIFT特征可以有效实现对鳞翅目昆虫图像的识别。     

界面发酵红曲霉的图像解析*

利用计算机视觉技术研究了红曲霉界面发酵过程中菌体形态变化与菌体生长的关系。在界面培养中通过检测前期菌落面积,后期隆起部分的表征体积——基于颜色变化的生长点分布,可以有效表示菌体生长状况。基于此建立了含有相应形态参数的动力学模型,该模型与常规动力学模型具有相似的表达形式。     

猕猴桃属植物果实营养成分的研究

本文报道猕猴桃属（Ａｃｔｉｎｉｄｉａ）３５个种类的果实的主要营养成分含量,猕猴桃果实维生素Ｃ的含量范围在１２．５４～１４０４．５２ｍｇ／１００ｇＦ．Ｗ,可溶性固形物含量范围在５．０％～１５．８％,总糖含量在０．９３％～９．０６％,总酸含量在０．２９％～２．５７％。果实最适宜的糖酸比为５～７,果实干物质１７种氨基酸含量范围在１．７９４％～９．０４％。文章还讨论了猕猴桃果实营养成分含量与开发利用价值的关系、果实的糖酸比与果实风味品质的关系、果肉汁液颜色与营养成分含量的关系等。     

Identification of Cichlid Fishes from Lake Malawi Using Computer Vision

Background

The explosively radiating evolution of cichlid fishes of Lake Malawi has yielded an amazing number of haplochromine species estimated as many as 500 to 800 with a surprising degree of diversity not only in color and stripe pattern but also in the shape of jaw and body among them. As these morphological diversities have been a central subject of adaptive speciation and taxonomic classification, such high diversity could serve as a foundation for automation of species identification of cichlids.

Methodology/Principal Finding

Here we demonstrate a method for automatic classification of the Lake Malawi cichlids based on computer vision and geometric morphometrics. For this end we developed a pipeline that integrates multiple image processing tools to automatically extract informative features of color and stripe patterns from a large set of photographic images of wild cichlids. The extracted information was evaluated by statistical classifiers Support Vector Machine and Random Forests. Both classifiers performed better when body shape information was added to the feature of color and stripe. Besides the coloration and stripe pattern, body shape variables boosted the accuracy of classification by about 10%. The programs were able to classify 594 live cichlid individuals belonging to 12 different classes (species and sexes) with an average accuracy of 78%, contrasting to a mere 42% success rate by human eyes. The variables that contributed most to the accuracy were body height and the hue of the most frequent color.

Conclusions

Computer vision showed a notable performance in extracting information from the color and stripe patterns of Lake Malawi cichlids although the information was not enough for errorless species identification. Our results indicate that there appears an unavoidable difficulty in automatic species identification of cichlid fishes, which may arise from short divergence times and gene flow between closely related species.     

Optimized lightweight CA-transformer: Using transformer for fine-grained visual categorization

As a rapidly developing research direction in computer vision (CV), related algorithms such as image classification and object detection have achieved inevitable research progress. Improving the accuracy and efficiency of algorithms for fine-grained identification of plant diseases and birds in agriculture is essential to the dynamic monitoring of agricultural environments. In this study, based on the computer vision detection and classification algorithm, combined with the architecture and ideas of the CNN model, the mainstream Transformer model was optimized, and then the CA-Transformer (Transformer Combined with Channel Attention) model was proposed to improve the ability to identify and classify critical areas. The main work is as follows: (1) The C-Attention mechanism is proposed to strengthen the feature information extraction within the patch and the communication between feature information so that the entire network can be fully attentive while reducing the computational overhead; (2) The weight-sharing method is proposed to transfer parameters between different layers, improve the reusability of model data, and at the same time increase the knowledge distillation link to reduce problems such as excessive parameters and overfitting; (3) Token Labeling is proposed to generate score labels according to the position of each Token, and the total loss function of this study is proposed according to the CA-Transformer model structure. The performance of the CA-Transformer model proposed in this study is compared with the current mainstream models on datasets of different scales, and ablation experiments are performed. The results show that the accuracy and mIoU of the CA-Transformer proposed in this study reach 82.89% and 53.17MS, respectively, and have good transfer learning ability, indicating that the model has good performance in fine-grained visual categorization tasks and can be used in ecological information. In the context of more diverse ecological information, this study can provide reference and inspiration for the practical application of information.     

Enhanced radial basis function neural network for tomato plant disease leaf image segmentation

Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease prediction easy in agriculture. Tomato crop front side leaf images are considered for research due to their high exposure to diseases. The image segmentation process assumes a significant role in identifying disease affected areas on tomato leaf images. Therefore, this paper develops an efficient tomato crop leaf disease segmentation model using an enhanced radial basis function neural network (ERBFNN). The proposed ERBFNN is enhanced using the modified sunflower optimization (MSFO) algorithm. Initially, the noise present in the images is removed by a Gaussian filter followed by CLAHE (contrast-limited adaptive histogram equalization) based on contrast enhancement and un-sharp masking. Then, color features are extracted from each leaf image and given to the segmentation stage to segment the disease portion of the input image. The performance of the proposed ERBFNN approach is estimated using different metrics such as accuracy, Jaccard coefficient (JC), Dice's coefficient (DC), precision, recall, F-Measure, sensitivity, specificity, and mean intersection over union (MIoU) and are compared with existing state-of-the-art methods of radial basis function (RBF), fuzzy c-means (FCM), and region growing (RG). The experimental results show that the proposed ERBFNN segmentation model outperformed with an accuracy of 98.92% compared to existing state-of-the-art methods like RBFNN, FCM, and RG, as well as previous research work.     

Color discrimination by the cotton-top tamarin (Saguinus oedipus oedipus) and its relation to fruit coloration

Old World monkeys and apes have been reported to differ from New World monkeys in their abilities to discriminate colors across the visible spectrum. Old World monkeys and apes (Macaca, Pan, Pongo) discriminate colors quite accurately, while some New World monkeys studied (Saimiri, Cebus) have shown lower sensitivity to and poorer discrimination of long wavelength light. This study examined the color discrimination ability of another New World primate, the cotton-top tamarin, Saguinus oedipus oedipus (family Callitrichidae). The tamarins were trained to discriminate a set of Munsell color chips, both within the same hue category and from the 2 hue categories on either side of the training hue. Results indicated that the cotton-top tamarin can make accurate discriminations across the visible spectrum. Human subjects were tested under similar conditions in order to compare their color discrimination abilities to those of the tamarins. The tamarins and human subjects had the most difficulty discriminating the same hues. The discrimination abilities of the monkeys were assessed in relation to the coloration of fruits eaten in a natural environment. A list of the species of fruits commonly eaten by various species of New World monkeys was compiled and the coloration of fruits at maturity was noted. It was found that most New World primate species eat fruits whose mature coloration ranges across most of the spectrum.     

肺癌细胞学亚型巴氏染色的计算机图像色度学定量分析

用计算机图像处理技术,对痰涂片中巴氏染色的角化性鳞癌细胞（ＫＳＣＣ）、非角化性鳞癌细胞（ＮＫＳＣＣ）和腺癌细胞（ＡＣＣ）胞浆进行了色度学定量分析。测试参数为红（Ｒ）、绿（Ｇ）、蓝（Ｂ）三基包含量和Ｒ、Ｇ、Ｂ的三色系数,同时还测算了胞浆的色度、饱和度、亮度和灰度,并进行了逐步判别分析。结果表明：ＫＳＣＣ的Ｒ、Ｇ、Ｂ及其三色系数、色度、饱和度与ＮＫＳＣＣ和ＡＣＣ相比差异非常显著;三基色的改变较灰度敏感．用三色系数、色度、饱和度对ＫＳＣＣ与ＮＫＳＣＣ、ＫＳＣＣ与ＡＣＣ进行计算机判别,准确度可达９５．３％和９７．１％。这些结果提示：三基色、三色系数及色度、饱和度分析对判别ＫＳＣＣ与ＮＫＳＣＣ、ＫＳＣＣ与ＡＣＣ有重要价值。对有关问题进行了讨论。     

DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection

Plant-leaf disease detection is one of the key problems of smart agriculture which has a significant impact on the global economy. To mitigate this, intelligent agricultural solutions are evolving that aid farmer to take preventive measures for improving crop production. With the advancement of deep learning, many convolutional neural network models have blazed their way to the identification of plant-leaf diseases. However, these models are limited to the detection of specific crops only. Therefore, this paper presents a new deeper lightweight convolutional neural network architecture (DLMC-Net) to perform plant leaf disease detection across multiple crops for real-time agricultural applications. In the proposed model, a sequence of collective blocks is introduced along with the passage layer to extract deep features. These benefits in feature propagation and feature reuse, which results in handling the vanishing gradient problem. Moreover, point-wise and separable convolution blocks are employed to reduce the number of trainable parameters. The efficacy of the proposed DLMC-Net model is validated across four publicly available datasets, namely citrus, cucumber, grapes, and tomato. Experimental results of the proposed model are compared against seven state-of-the-art models on eight parameters, namely accuracy, error, precision, recall, sensitivity, specificity, F1-score, and Matthews correlation coefficient. Experiments demonstrate that the proposed model has surpassed all the considered models, even under complex background conditions, with an accuracy of 93.56%, 92.34%, 99.50%, and 96.56% on citrus, cucumber, grapes, and tomato, respectively. Moreover, the proposed DLMC-Net requires only 6.4 million trainable parameters, which is the second best among the compared models. Therefore, it can be asserted that the proposed model is a viable alternative to perform plant leaf disease detection across multiple crops.