ALGORITHMS OF THE AUTOMATIC DIGITAL ANALYSIS OF VEGETABLE SEEDS QUALITY by F. В. Musaev, М. S. Antoshkina, A. V. Soldatenko, S. L. Beletsky, N. N. Potrakhov

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Detection of Seed Potato Sprouts Based on Improved YOLOv8 Algorithm by Yufei Li, Qinghe Zhao, Zifang Zhang, Jinlong Liu, Junlong Fang

“…A fast and efficient object recognition algorithm is required for the additional removal process to identify unqualified seed potatoes. …”
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Machine Learning Algorithms for Nondestructive Sensing of Moisture Content in Grain and Seed by Arthur P. LeBlanc, Samir Trabelsi, Khaled Rasheed, John A. Miller

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Optical detection of single and multiple seeding using an innovative shape-recognition algorithm by Ali Ghaffarnezhad, Hossein Navid, Hadi Karimi

“…In this research, a novel shape-based algorithm is presented that utilizes seven pairs of 3-mm infrared LEDs to efficiently recognize and count both single seeds and overlapping seeds. …”
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A METHOD FOR MONITORING RICE SEED LOSS BASED ON WOA-BP ALGORITHM by Jin Chen, Ting Shi, Yaoming Li, Yahui Zhu, Caoyuan Niu

“…Aiming at the problems of the slow response speed and low monitoring accuracy of the existing domestic seed loss rate monitoring models, this paper proposed a rice seed loss rate monitoring method based on the whale optimization algorithm-back propagation neural network (WOA-BP). …”
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Object-oriented remote sensing image segmentation based on automatic multiseed region growing algorithm by YAN Dong-yang, MING Dong-ping

“…For the segmentation of a remote sensing image, the seeded region growing algorithm is a common method. …”
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A survey of sequence-to-graph mapping algorithms in the pangenome era by Yingbo Cui, Chenchen Peng, Zeyu Xia, Canqun Yang, Yifei Guo

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Influence maximization algorithm based on social network by Xuan WANG, Yu ZHANG, Junfeng ZHOU, Ziyang CHEN

“…The influence maximization (IM) problem asks for a group of seed users in a social network under a given propagation model, so that the information spread is maximized through these users.Existing algorithms have two main problems.Firstly, these algorithms were difficult to be applied in large-scale social networks due to limited expected influence and high time complexity.Secondly, these algorithms were limited to specific propagation models and could only solve the IM problem under a single type of social network.When they were used in different types of networks, the effect was poor.In this regard, an efficient algorithm (MTIM) based on two classic propagation models and reverse influence sampling (RIS) was proposed.To verify the effectiveness of MTIM, experiments were conducted to compare MTIM with greedy algorithms such as IMM, TIM and PMC, and heuristic algorithms such as OneHop and Degree Discount on four real social networks.The results show that MTIM can return a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow><mo>(</mo> <mrow> <mn>1</mn><mo>−</mo><mfrac> <mn>1</mn> <mtext>e</mtext> </mfrac> <mo>−</mo><mi>ε</mi></mrow> <mo>)</mo></mrow></math></inline-formula> approximate solution, effectively expand the expected influence and significantly improve the efficiency.…”
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A Hybrid Seed Node Selection and No-Retracing Random Walk in Page Rank Algorithm by Azam Bastanfard, Ali Kheradbeygi Moghadam, Ali Fallahi RahmatAbadi

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Development and Comparison of Interrupt-Based and Analog-to-Digital Converter Algorithms for Seed Counting in Precision Planters by A. Ghaffarnezhad, H. Navid, H. Karimi

“…Both algorithms effectively counted seeds larger in diameter than the distance between adjacent LEDs with remarkable accuracy. …”
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Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms? by Khabat Khosravi, Aitazaz A. Farooque, Sayed M. Bateni, Changhyun Jun, Dorsa Mohammadi, Zahra Kalantari, James R. Cooper

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Heterogeneous redundancies scheduling algorithm for mimic security defense by Qinrang LIU, Senjie LIN, Zeyu GU

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Human fall direction recognition in the indoor and outdoor environment using multi self-attention RBnet deep architectures and tree seed optimization by Awais Khan, Jung-Yeon Kim, Chomyong Kim, Muhammad Attique Khan, Hyojin Shin, Jiyoung Woo, Yunyoung Nam

“…The 7-RBNet and 9-RBNet self-attention models demonstrated superior accuracy and precision rates, leading us to exclude the 3-RBNet self model from further analysis. To optimize feature selection and improve classification performance while reducing computational costs, we employed the tree seed algorithm on the self-attention features of 7-RBNet and 9-RBNet self-attention models. …”
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Using UAV-based multispectral images and CGS-YOLO algorithm to distinguish maize seeding from weed by Boyi Tang, Jingping Zhou, Chunjiang Zhao, Yuchun Pan, Yao Lu, Chang Liu, Kai Ma, Xuguang Sun, Ruifang Zhang, Xiaohe Gu

“…Compared with YOLOv8, YOLOv6, YOLOv5, and YOLOv3, the CGS-YOLO algorithm has improved mAP by 3.8, 4.2, 4.5 and 6.6 percentage points, respectively. …”
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An adaptive transition probability matrix with quality seeds for cellular automata models by Youcheng Song, Xu Hongtao, Haijun Wang, Ziyang Zhu, Xinyi Kang, Xiaoxu Cao, Zhang Bin, Haoran Zeng

“…Addressing this gap, our research introduces the adaptive transition probability matrix with quality seeds (ATPMS) model, which incorporates both the Markov model and the genetic algorithm (GA) into the traditional CA framework. …”
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Seed multispectral imaging combined with machine learning algorithms for distinguishing different varieties of lettuce (Lactuca sativa L.) by Jinpeng Wei, Zhangyan Dai, Qi Zhang, Le Yang, Zhaoqi Zeng, Yuliang Zhou, Jun Liu, Bingxian Chen

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Random forest algorithms: a tool to identify the impact of arbuscular mycorrhizal fungi inoculation, seed maturation stage and geographic diversity of Pimpinella anisum L. accessio... by Bruno Rodrigues de Oliveira, Alan Mario Zuffo, Francisco Charles dos Santos Silva, Fábio Steiner, Amal Mohamed AlGarawi, Mohammad K. Okla, Mousa Nhs, Yousef Alhaj Hamoud, Izabela Josko, Mohamed S. Sheteiwy, Mohamed Salem Alyafei, Saad Sulieman

“…Seeds were harvested at three different maturity stages: mature seeds (157 d after sowing), premature seeds (147 d after sowing), and immature seeds (137 d after sowing). …”
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Corn Seed Freezing Damage Identification of Different Sides Based on Hyperspectral Imaging and SPA-2DCOS Fusion Algorithm by Jun Zhang, Limin Dai, Ruiyuan Zhuang

“…In order to improve the utilization efficiency of corn seeds and meet the demand of single-seed seeding technology in agriculture, this study was conducted to explore the effect of freezing damage detection on the endosperm and embryo sides of single corn seeds, based on hyperspectral imaging combined with a feature fusion algorithm and a machine learning method. …”
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Evaluation of Image Processing Technique on Quality Properties of Chickpea Seeds (Cicer arietinum L.) Using Machine Learning Algorithms by Halil Kırnak, Necati Çetin, İhsan Serkan Varol

“…In this study, effects of seven different irrigation treatments (I1-rainfed, I2-pre-flowering single irrigation, I3-beginning of flowering single irrigation, I4-50% pod set single irrigation, I5-irrigation at 50% flowering and 50% pod fill, I6-irrigation before flowering and at 50% pod set, I7-full irrigation) on size, shape, mass, and color properties of chickpea seeds were investigated, and machine learning algorithms were used to estimate mass and color attributes of chickpea seeds. …”
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DCS-YOLOv5s: A Lightweight Algorithm for Multi-Target Recognition of Potato Seed Potatoes Based on YOLOv5s by Zhaomei Qiu, Weili Wang, Xin Jin, Fei Wang, Zhitao He, Jiangtao Ji, Shanshan Jin

“…Given the dearth of research on intelligent tuber-cutting machinery in China, particularly concerning the identification of bud eyes and defect detection, this study has developed a multi-target recognition approach for potato seed tubers utilizing deep learning techniques. By refining the YOLOv5s algorithm, a novel, lightweight model termed DCS-YOLOv5s has been introduced for the simultaneous identification of tuber buds and defects. …”
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