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In this paper, a Multi-scale Contrastive Graph Convolutional Network (MC-GCN) method is proposed for unconstrained face recognition with image sets, which takes a set of media (orderless images and videos) as a face subject instead of single media (an image or video). Due to factors such as illumination, posture, media source, etc., there are huge intra-set variances in a face set, and the importance of different face prototypes varies considerably. How to model the attention mechanism according to the relationship between prototypes or images in a set is the main content of this paper. In this work, we formulate a framework based on graph convolutional network (GCN), which considers face prototypes as nodes to build relations. Specifically, we first present a multi-scale graph module to learn the relationship between prototypes at multiple scales. Moreover, a Contrastive Graph Convolutional (CGC) block is introduced to build attention control model, which focuses on those frames with similar prototypes (contrastive information) between pair of sets instead of simply evaluating the frame quality. The experiments on IJB-A, YouTube Face, and an animal face dataset clearly demonstrate that our proposed MC-GCN outperforms the state-of-the-art methods significantly. [ABSTRACT FROM AUTHOR]

Background: Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results: On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion: The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science. [ABSTRACT FROM AUTHOR]

• Thousands of weed images are acquired and medium scale dataset is built at seedling stage of cotton field scenario. • The proposed deep learning model for weed detection in cotton field at seedling stage is fast and accurate. • A vision system attached to the scene perception mechanism of intelligent robot in farmland maintains good performance. • The online weed detection and spraying experiments of the intelligent spraying robot in field condition are prospective. The precision spraying robot dispensing herbicides only on unwanted plants based on machine vision detection is the most appropriate approach to ensure the sustainable agro-ecosystem and the minimum impact of nuisance weeds. However, the coexistence of crops and weeds, the similarities of plants and the multi-scale attribute of weeds make reliable detection difficult, leading to serious limitations in the application of deep learning method to target spraying in the field environment. In this paper, 4694 representative images are acquired from cotton field scenario as the data basis for deep learning model. A novel weed detection model is constructed by employing CBAM module, BiFPN structure and Bilinear interpolation algorithm. The proposed network can effectively learn the deep information and distinguish weeds from cotton seedlings in various complicated growth states. Evaluation experiments on our constructed dataset indicate that the proposed method reaches an mAP of 98.43% with faster inference speed than Faster R-CNN. Our proposed weed detection model is also deployed in spraying robot that we developed ourselves, and field trials are conducted for detection and spraying, which could maintain the excellent performance with mAP of 97.42% and effective spraying rate of 98.93%. The ability to successfully execute the weed detection and herbicide spraying management in the field lays foundation for targeted spraying in precision weed control, which has an excellent impact on cotton cultivation and growth. [ABSTRACT FROM AUTHOR]

Abstract Inspired by the concept of memory mechanism and predictive coding from the cognitive neuroscience, this paper presents a deep memory and prediction neural network (DMPNet) for video prediction. Correspondingly, memory and error propagation units are designed in DMPNet to capture the previous spatial-temporal information and compute current predictive error which is forwarded to the prediction unit for correcting the subsequent video prediction. Subsequently, prediction unit takes the information stored in memory unit and predictive error of previous frame as input to predict the next frame. We evaluate our method on two public real-world datasets and demonstrate that the proposed DMPNet outperforms some state-of-the-art methods quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

• A novel hybrid model (MTYOLOX) for tree-level apple inflorescences detection. • Multi-transformers-based CNN structure was designed to improve the detection effect. • The proposed model shows competitive performance compared with different models. Fruit tree-specific management plays a vital role in ensuring fruit quality and quantity. Monitoring inflorescence information is essential for apple tree cultivation activity such as inflorescence thinning in the early flowering phase. The proportion to be thinned is mainly determined by the inflorescence intensity of the whole tree. Most existing researches pay more attention to apple flower detection in local regions of the tree, but a small subset of trees cannot provide tree-level information on inflorescences. Therefore, a novel transformer-based CNN model, MTYOLOX, is presented for robustly detecting full tree inflorescences. The DAT-Darknet and ST-PAFPN modules based on multiple self-attention mechanisms are designed and rationally embedded into the backbone and neck of the network to explore the potential global context information and extract more distinguished features for inflorescences detection. The MTYOLOX exhibits robust adaptability to variable illumination directions in the uncontrolled and challenging orchard environment. Comparisons with representative detection methods (SSD, FCOS, Faster RCNN, YOLOv4, YOLOv5s, Variant YOLOv5s, YOLOXs and YOLOv7) show that the proposed MTYOLOX achieves the highest AP 50 of 0.834 and AR 50 of 0.933 with encouraging detection speed, total parameters, flops and model size. The competitive performance indicated that the proposed method is feasible for apple inflorescences detection of the whole tree. On the basis of detection results from the MTYOLOX, the tree-level inflorescences density mapping is implemented for the potential commercial application. This study is expected to provide a solution reference for precision horticulture. [ABSTRACT FROM AUTHOR]

Veins are a critical component of the plant growth and development system, playing an integral role in supporting and protecting leaves, as well as transporting water, nutrients, and photosynthetic products. A comprehensive understanding of the form and function of veins requires a dual approach that combines plant physiology with cutting-edge image recognition technology. The latest advancements in computer vision and machine learning have facilitated the creation of algorithms that can identify vein networks and explore their developmental progression. Here, we review the functional, environmental, and genetic factors associated with vein networks, along with the current status of research on image analysis. In addition, we discuss the methods of venous phenotype extraction and multi-omics association analysis using machine learning technology, which could provide a theoretical basis for improving crop productivity by optimizing the vein network architecture. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for robust Sign Language Recognition from RGB-D data. A Sparse Observation (SO) description is proposed to character each sign in terms of the typical hand postures. Concretely speaking, the SOs are generated by considering the typical posture fragments, where hand motions are relatively slow and hand shapes are stable. Thus the matching between two sign words is converted to measure the similarity computing between two aligned SO sequences. The alignment is formulated as a variation of Stable Marriage Problem (SMP). The classical “propose-engage” idea is extended to get the order preserving matched SO pairs. In the training stage, the multiple instances from one sign are fused to generate single SO template. In the recognition stage, SOs of each probe sign “propose” to SOs of the templates for the purpose of reasonable similarity computing. To further speed up the SO alignment, hand posture relationship map is constructed as a strong prior to generate the distinguished low-dimensional feature of SO. Moreover, to get much better performance, the motion trajectory feature is integrated. Experiments on two large datasets and an extra Chalearn Multi-modal Gesture Dataset demonstrate that our algorithm has much higher accuracy with only 1/10 time cost compared with the HMM and DTW based methods. [ABSTRACT FROM AUTHOR]

• Proposing an end-to-end noise-tolerant feature fusion network to integrate outdoor RGB-D information. • Designing an attention-based fusion module to eliminate the negative influence of depth noise and make full use of the complementary multi-modal features. • Learning object position from multi-scale features from color images and fusion modules, further improving the network noise immunity. • Achieving accurate fruit detection in outdoor scenes with a real-time speed. In the process of farm automation, fruit detection is the basis and guarantee for yield prediction, automatic picking, and other orchard operations. RGB images can only obtain the two-dimensional information of the scene, which is not sufficient to effectively distinguish fruits that are dense growth and occlusion by branches and leaves. With the development of depth sensors, using RGB-D images with more complementary information can boost the performance of fruit detection. However, due to the nature of sensors and scene configurations, the quality of outdoor depth images is poor, posing a challenge when fusing RGB-D features. Therefore, this paper proposes an end-to-end RGB-D object detection network, termed as noise-tolerant feature fusion network (NT-FFN), to utilize the outdoor multi-modal data properly and improve the detection accuracy. Specifically, the NT-FFN first uses two structurally identical feature extractors to extract single-modal (color and depth) features, which is the base of the subsequent feature fusion. Then, to avoid introducing too much depth noise and focus the perception on the important part of the features, an attention-based fusion module is designed to adaptively fuse the multi-modal features. Finally, multi-scale features from the color images and the fusion modules are used to predict object position, which not only improves the network's ability to detect multi-scale objects but also further enhances the noise immunity of the network. In addition, this paper constructs an RGB-D citrus fruit dataset, which contributes to comprehensively evaluating the proposed network. Evaluation metrics on the dataset show that the NT-FFN achieves an AP50 of 95.4% with a real-time speed, which outperforms single-modal methods, common multi-modal fusion strategies, and advanced multi-modal detection methods. The proposed NT-FFN also achieves excellent detection results in other fruit detection tasks, which verifies its generalization ability. This study provides the possibility and foundation for performing multi-modal information fusion in outdoor fruit detection. [ABSTRACT FROM AUTHOR]

• Describing overall features by capturing apple images from multiple perspectives. • Lightweight CNN helps model more practical. • Aggregating spatial apple features is realized by bilateral long short-term memory. • The performance and efficiency are far excellent with a 99.23% accuracy. Apple grading is one of the typical tasks in food grading. For the image-based apple grading task, the variety of apple shapes and the incompleteness of single-view visual information pose great difficulties in achieving high accuracy of apple grading. This paper proposes a novel multi-view spatial network to tackle the apple grading task, which incorporates apple size information as one of the grading criteria, to solve those problems that affect the accuracy of grading. Specifically, we first use well-pretrained lightweight CNNs to extract low-level features of each view of apple. Second, we formulate a spatial feature aggregation module through a bidirectional LSTM and mean-pooling to exploit the correlative information of apple from multiple views. Besides, we elaborately design a multi-view apple data collection scheme for apple grading dataset construction. The experimental results indicate that the accuracy of Multi-View Spatial Network reaches 99.23%, which is quite promising in apple grading task and outperforms some state-of-the-art grading methods. [ABSTRACT FROM AUTHOR]

Exact yield estimation of fruits on plants guaranteed fine and timely decisions on harvesting and marketing practices. Automatic yield estimation based on unmanned agriculture offers a viable solution for large orchards. Recent years have witnessed notable progress in computer vision with deep learning for yield estimation. Yet, the current practice of vision-based yield estimation with successive frames may engender fairly great error because of the double counting of repeat fruits in different images. The goal of this study is to provide a wise framework for fruit yield estimation in sequence images. Specifically, the anchor-free detection architecture (CenterNet) is utilized to detect fruits in sequence images from videos collected in the apple orchard and orange orchard. In order to avoid double counts of a single fruit between different images in an image sequence, the patch matching model is designed with the Kuhn–Munkres algorithm to optimize the paring process of repeat fruits in a one-to-one assignment manner for the sound performance of fruit yield estimation. Experimental results show that the CenterNet model can successfully detect fruits, including apples and oranges, in sequence images and achieved a mean Average Precision (mAP) of 0.939 under an IoU of 0.5. The designed patch matching model obtained an F1-Score of 0.816 and 0.864 for both apples and oranges with good accuracy, precision, and recall, which outperforms the performance of the reference method. The proposed pipeline for the fruit yield estimation in the test image sequences agreed well with the ground truth, resulting in a squared correlation coefficient of R2apple = 0.9737 and R2orange = 0.9562, with a low Root Mean Square Error (RMSE) for these two varieties of fruit. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel method to improve the accuracy of head pose estimation. Since biologically inspired features (BIF) have been demonstrated to be both effective and efficient for many visual tasks, we argue that BIF can be applied to the problem of head pose estimation. By combining the BIF with the well-known local binary pattern (LBP) feature, we propose a novel feature descriptor named “local biologically inspired features” (LBIF). Considering that LBIF is extrinsically very high dimensional, ensemble-based supervised methods are applied to reduce the dimension while at the same time improving its discriminative ability. Results obtained from the evaluation on two different databases show that the proposed LBIF feature achieves significant improvements over the state-of-the-art methods of head pose estimation. [Copyright &y& Elsevier]

• A novel model Attention-guided Data Enrichment Network (ADEN) for the visual categorization of apple bud-types was proposed. • Attention-based Deep Convolutional Neural Networks (DCNN) structure was designed to improve the classification effect. • The proposed model shows the promising performance compared with different models. The number of flower buds on the apple tree is the crucial factor for fruit load determining, thus the essence of apple tree pruning is bud removal. Most horticulture activities in apple orchards at present primarily rely on skilled farmers. However, distinguishing between different types of apple buds is still hard work for many planters due to their similar appearances. The most recent published works have proven the superiority of computer vision and deep learning in image recognition tasks. Deep convolutional neural network (DCNN) is an efficient type of network in deep learning architecture for visual features analysis. To categorize types of apple bud at the fine-grained level, a DCNN-based visual classification model denoting the attention-guided data enrichment network (ADEN) is proposed. Specifically, in ADEN, the ResNeSt50 network is used as the feature extractor module for characterizing the apple bud trait from each input image. Based on attention maps, the attention-guide data enrichment module, containing attention-guided CutMix and attention-guided erasing, is designed for the task of enriching training samples via dropout and fusing local features of images, which further improves the training efficiency and discriminative ability of the classifier. All the experiments are conducted on the orchard-shot image dataset contained two classes of apple buds, include the flower bud and the leaf bud. The proposed method conveys a consistent and significant improvement in performance and achieves testing accuracy of 92.39% with satisfying precision, recall and f1-score, which outperformed the comparative models. The proposed method can readily realize accurate identification for bud-types of apples and is helping to promote the advancement of pruning and training robotization in orchards. [ABSTRACT FROM AUTHOR]

• Proposing a top-down keypoint detection method to assist pruning bearing branch while picking fruit. • Generating candidate area by extending the citrus area to the citrus stem direction. • Fusing multi-level features from the same and different feature scales to extract information of bearing branch keypoint in the candidate area. • Achieving accurate detection of bearing branch keypoint with a smaller model size and lower computing cost. Automated orchard operation has been a firm goal of fruit farmers for a long time. Deep learning-based approaches have been widely used to improve the performance of fruit detection, branch pruning, production estimating and other agricultural operations. This paper proposes a novel method to detect keypoint on the branch, which enables branch pruning during fruit picking. Specifically, a top-down framework for bearing branch keypoint detection is developed. First, a candidate area is generated according to the fruit-growing position and the fruit stem keypoint detection, which provides an attention region for further keypoint detection. Second, a multi-level feature fusion network which combines features in the same spatial sizes (intra-level) and from different spatial sizes (inter-level) is proposed to detect keypoint within the candidate area. The network can learn the spatial and semantic information and model the relationship among bearing branch keypoints. In addition, this paper constructs a citrus bearing branch dataset, which contributes to comprehensively evaluating the proposed method. Evaluation metrics on the dataset indicate the proposed method reaches an AP of 77.4% and an accuracy score of 84.7% with smaller model size and lower computing power consumption, which significantly outperforms several state-of-the-art keypoint detection methods. This study provides the possibility and foundation for performing automatic branch pruning during fruit harvesting. [ABSTRACT FROM AUTHOR]

Abstract: Automatically locating facial landmarks in images is an important task in computer vision. This paper proposes a novel context modeling method for facial landmark detection, which integrates context constraints together with local texture model in the cascaded AdaBoost framework. The motivation of our method lies in the basic human psychology observation that not only the local texture information but also the global context information is used for human to locate facial landmarks in faces. Therefore, in our solution, a novel type of feature, called Non-Adjacent Rectangle (NAR) Haar-like feature, is proposed to characterize the co-occurrence between facial landmarks and its surroundings, i.e., the context information, in terms of low-level features. For the locating task, traditional Haar-like features (characterizing local texture information) and NAR Haar-like features (characterizing context constraints in global sense) are combined together to form more powerful representations. Through Real AdaBoost learning, the most discriminative feature set is selected automatically and used for facial landmark detection. To verify the effectiveness of the proposed method, we evaluate our facial landmark detection algorithm on BioID and Cohn-Kanade face databases. Experimental results convincingly show that the NAR Haar-like feature is effective to model the context and our proposed algorithm impressively outperforms the published state-of-the-art methods. In addition, the generalization capability of the NAR Haar-like feature is further validated by extended applications to face detection task on FDDB face database. [Copyright &y& Elsevier]

Quality grading and sorting are crucial post-harvest procedures to increase the market value of crops. To reduce the over-dependence on manual work for these tasks, automated sorting methods have been studied. However, we find existing research focuses mainly on round-like fruits and vegetables, and most machines can only grade one object at a time in their vision pipeline. To address these limitations, this paper proposes a comprehensive framework specifically for detecting and analyzing rod-like crops based on multi-object oriented detection. Zizania shoots are primarily used to validate our methods. To implement the deep learning models, an efficient oriented bounding box label software called OBBLabel is developed, and several large-scale image datasets are constructed. Both of the software and datasets are open-sourced for the community. Based on YOLOv8 architecture, the proposed YOLO-OBB model predicts oriented bounding boxes to extract all rod-like targets in image with 0.903 mAP@0.5. A multi-label recognition model YOLO-MLD then conducts quality grading and posture perception on individual target with 93.4% mean accuracy. Thus, precise position and quality information for all objects can be obtained in near real-time for subsequent suction cup-based sorting operations. Furthermore, according to the framework, this paper designs and manufactures a prototype sorting machine with edge intelligence for rod-like crops. • Framework for rod-like crops sorting with dataset, software, algorithms and machine. • Oriented detection model detects arbitrary-pose targets by oriented bounding boxes. • Multi-label recognition model parses the grade and posture of extracted single crop. • Machine with edge intelligence automates the workflow from sorting to packaging. • Annotation software and labeled datasets are open-sourced. [ABSTRACT FROM AUTHOR]

• A method for estimating citrus 3D pose via a single RGB image is developed. • An image annotation tool to label the fruit 3D pose in a 2D image is designed. • The fruit navel point is detected and the direction of fruit rotation is predicted. • The demonstrated performance shows that it is helpful for non-destructive harvesting. Automated fruit harvesting is promising research in the development of agricultural modernization. However, the complex and non-structural orchard environment is extremely challenging. In order to meet the needs of different end-effectors and to improve the success rate of automatic fruit harvesting, it is critical to perform fruit pose estimation before picking operations. In this study, a citrus pose estimation method through a single RGB image is introduced. The rotation of the citrus pose is defined as a vector that passes through the center of the fruit, which is perpendicular to the plane where the fruit navel point is located. Simply speaking, a multi-task learning model named FPENet is proposed to simultaneously locate the fruit navel point and predict the fruit rotation vector. And a hyperparameter is introduced in the loss function to achieve the simultaneous convergence of multiple tasks. In addition, this paper designs a 2D image annotation tool and constructs a citrus pose dataset, which contributes to model training and also the algorithm evaluation. In the experiment, we evaluate and analyze each module of the proposed network structure, and verify its performance on a harvesting robot. The experimental results show that the FPENet achieves an 88.92 AP score on fruit navel point detection, and 11.13 ° on the average error of the rotation vector. Over 90% of rotation vectors have an angular error of less than 22.5 °. The harvesting success rate is 79.79%. This study offers a new idea for fruit pose estimation and provides the possibility and foundation for estimating fruit pose with a 2D image input. [ABSTRACT FROM AUTHOR]

• Vision system detects the position of overlapping mushrooms. • Multi-stage flexible end-effector improves picking success and breakage rates. • Classification of mushroom clusters based on growth density. • Intelligent picking planning strategy reduces overall picking time. • Integrated robot is fully field-tested with good results. The disorganized and densely growing Agaricus bisporus remains challenging for robotic harvesting. Aiming at the lower efficiency, lower success rate, and higher breakage rate in the robotic harvesting process, we propose a fully integrated, autonomous, and innovative harvesting robot to overcome the challenges due to the dense growth characteristics of Agaricus bisporus. An overlapping target detection vision system based on the yolov5s deep learning network is developed to improve the detection accuracy of dense and hidden mushrooms. An intelligent picking planning strategy by fusing multiple algorithms is formulated to guide the robot to effectively pick mushroom clusters with different growth characteristics. A multi-stage telescopic flexible end-effector is designed to pick mushrooms in a narrow space and reduce the picking breakage rate. Finally, we successfully developed a fully autonomous picking robotic system and evaluated it in a mushroom cultivation factory. Experimental results show that the robot can pick Agaricus bisporus continuously with an overall success rate of 94.1 % and an average picking time of 4.23 s per mushroom. These efforts provide a solid foundation for future applications of Agaricus bisporus picking robots. [ABSTRACT FROM AUTHOR]

Signer adaptation is important for sign language recognition systems because a fixed system cannot perform well on all kinds of signers. In supervised signer adaptation, the labeled adaptation data must be collected explicitly. To skip the data collecting process in signer adaptation, we propose a novel unsupervised adaptation method, namely the hypothesis comparison guided cross validation method. The method not only addresses the problem of the overlap between the data set to be labeled and the data set for adaptation, but also employs an additional hypothesis comparison step to decrease the noise rate of the adaptation data set. We also utilize linguistic prior knowledge to down sample the adaptation data list to further decrease the noise rate. To evaluate the effectiveness of the proposed method, the CASIIE-SL-Database is formed, which is the first specialized data set for unsupervised signer adaptation to the best of our knowledge. Experimental results show that the proposed method can achieve relative word error rate reductions of 3.93% and 4.05% respectively compared with self-teaching method and cross validation method. Though the method is proposed for signer adaptation, it can also be applied to speaker adaptation and writer adaptation directly. [ABSTRACT FROM AUTHOR]

Abstract: The dissimilarity between a target descriptor and a particle descriptor is a crucial parameter in the particle filtering (PF), while the widely used Bhattacharyya dissimilarity (BD) is not discriminative enough. This paper presents an incremental Bhattacharyya dissimilarity (IBD) for measuring histogram based descriptors (HBDs) used for particle weight estimation. IBD is defined by incorporating an incremental similarity matrix (ISM) into the BD. Such an ISM imposes the incremental similarity beliefs on the matched bin patches of two input histograms and enables a cross-bin interaction during the comparison, which yields the enhanced capability of discriminating the particles located in the object from those positioned in the background. We propose a robust approach to compute the ISM by jointly utilizing the spatial and temporal attributes. Also, to handle target appearance changes and deformations, a classification-inspired target model update strategy is presented. These components lead to an effective and robust tracking algorithm. Experimental results demonstrate that IBD shows promising discriminative capability in comparison with other state of the art dissimilarity measures. Moreover, the IBD based PF-tracker also exhibits competitive tracking performance, especially under scenarios of partial occlusion and background clutter. [Copyright &y& Elsevier]

[Display omitted] • A lightweight CNN model (LightNet) is presented to classify the quality of zizania. • A compressed block is designed to reduce the computation complexity. • Extensive experiments are conducted with the automatic grading device. Zizania, one of aquatic vegetables, needs to be graded before entering market for assuring the product quality. However, it is time-consuming, tedious, labor-intensive, inaccurate and expensive to assess qualitatively and grade zizanias manually. This paper gives an effective solution to automatically classify fresh zizania into two categories, high quality and defective quality, by using the deep learnt features from the appearances. A new architecture of convolutional neural network, called LightNet, has been proposed and described. Specifically, it is composed of many compressed blocks, which is designed to reduce the computation complexity mainly by converting serial down-sampling and convolution operation to parallel structure. We evaluate the proposed architecture on the zizania image dataset collected by ourselves and integrate the algorithm in the automatic grading device. The experimental results show that the accuracy rate achieves 95.62% and the speed of inference quality is around 47 ms per zizania image. The proposed LightNet for automate classification has less parameters and lower computation complexity than popular networks, while maintaining the comparable accuracy in the task of grading zizanias. It obtains 99.31% accuracy in the task of grading apples. The result proves that it can be extended to other tasks about classification. [ABSTRACT FROM AUTHOR]