Artificial intelligence in seeding density optimization and yield simulation for oat by Eldair F. Dornelles, Adriana R. Kraisig, José A. G. da Silva, Sandro Sawicki, Fabricia Roos-Frantz, Roberto Carbonera

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Modulation instability control via evolutionarily optimized optical seeding by Sader Lynn, Boussafa Yassin, Hoang Van Thuy, Haldar Raktim, Kues Michael, Wetzel Benjamin

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Rice seed integrity evaluation: Developing a rapid onsite system to check seed fraud using a portable NIR spectroscopic device coupled with smartphone technology by Ernest Teye, Charles Lloyd Yeboah Amuah, Vida Gyimah Boadu, Kwadwo Anokye Dompreh, Maxwell Darko Asante, Francis Padi Lamptey, Stephen Narh, Daniel Dzorkpe Gamenyah, George Oduro Nkansah, Selorm Akaba

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REPrise: de novo interspersed repeat detection using inexact seeding by Atsushi Takeda, Daisuke Nonaka, Yuta Imazu, Tsukasa Fukunaga, Michiaki Hamada

“…Results In this study, we developed REPrise, a de novo interspersed repeat detection software program based on a seed-and-extension method. Although the algorithm of REPrise is similar to that of RepeatScout, which is currently the de facto standard tool, we incorporated three unique techniques into REPrise: inexact seeding, affine gap scoring and loose masking. …”
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Contact Parameter Calibration for Discrete Element Potato Minituber Seed Simulation by Kai Chen, Xiang Yin, Wenpeng Ma, Chengqian Jin, Yangyang Liao

“…With the application of the response surface method and a search algorithm based on Matlab 2019, the optimal combination of seed-to-seed contact parameters, namely, the collision recovery coefficient, static friction coefficient, and rolling friction coefficient, is obtained, which are 0.500, 0.476, and 0.043, respectively. …”
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Detection of sugar beet seed coating defects via deep learning by Abdullah Beyaz, Zülfi Saripinar

“…Abstract The global seed coating market is expected to experience substantial growth, increasing from a 2023 valuation of USD 2.0 billion to an estimated value of USD 3.1 billion by 2028. …”
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INSTRUMENTAL AND BIOPHYSICAL METHOD OF EVALUATION TEST OF SEEDS OF GREEN VEGETABLE CULTURES by F. B. Musayev, V. A. Kharchenko, M. S. Antoshkina

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Key Technologies in Intelligent Seeding Machinery for Cereals: Recent Advances and Future Perspectives by Wei Liu, Jinhao Zhou, Tengfei Zhang, Pengcheng Zhang, Mengjiao Yao, Jinhong Li, Zitong Sun, Guoxin Ma, Xinxin Chen, Jianping Hu

“…In the future, more advanced multi-algorithm and multi-sensor fusion technologies for soil property detection, optimal seeding rate decisions, seeding rates, and seed position control are likely to evolve. …”
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Competitive pricing and seed node selection in a two-echelon supply chain by Mohammad Hossein Morshedin, Seyed Jafar Sadjadi, Babak Amiri, Mahdi Karimi

“…To maximize her profit, the retailer decides based on three factors: first, the leader's decision about wholesale price; second, the social network structure, which is critical for selecting the seed nodes; and third, people's valuation of the product. …”
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Differentiation of Soybean Genotypes Concerning Seed Physiological Quality Using Hyperspectral Bands by Izabela Cristina de Oliveira, Dthenifer Cordeiro Santana, Victoria Toledo Romancini, Ana Carina da Silva Cândido Seron, Charline Zaratin Alves, Paulo Carteri Coradi, Carlos Antônio da Silva Júnior, Regimar Garcia dos Santos, Fábio Henrique Rojo Baio, Paulo Eduardo Teodoro, Larissa Ribeiro Teodoro

“…The data obtained were subjected to an analysis of variance and the means were compared by the Scott–Knott test at 5% probability, analyzed using R software version 4.2.3 (Auckland, New Zealand). The data on the physiological quality of the seeds of the soybean genotypes were subjected to principal component analysis (PCA) and associated with the K-means algorithm to form groups according to the similarity and distinction between the genetic materials. …”
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Determination of seed sowing quality by dotted-nesting method using video file processing technology by Strygin Sergei, Konkina Viktoriya, Korobova Irina, Rybachok Maksim, Pustovarov Nikita

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FSUNet: lightweight full-scale information fusion UNet for seed coat thickness measurement by Zhikun Zhang, Qin Xu, Haojie Shi, Guangwu Zhao, Lu Gao, Tao Wang, Guosong Gu, Liangquan Jia

“…In addition, we also provide a seed coat thickness measurement algorithm that can obtain stable and accurate measurement results. …”
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Application of Hyperspectral Imaging for Watermelon Seed Classification Using Deep Learning and Scoring Mechanism by Hengnian Qi, Mengbo He, Zihong Huang, Jianfang Yan, Chu Zhang

“…Subsequently, three deep learning models (LeNet, GoogLeNet, and ResNet) were used to classify 10 common watermelon seeds. …”
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Optimized wavelength selection for eggplant seed vitality classification using information acquisition techniques by Bing Yang, Xuyang Liu, Dongfang Zhang, Dongfang Zhang, Xiaofei Fan, Xiaofei Fan, Bo Peng, Jun Zhang, Jun Zhang

“…In response to the need for efficient and nondestructive assessment methods, this study explores the use of hyperspectral imaging combined with advanced feature selection and classification algorithms to evaluate eggplant seed viability. Hyperspectral imaging was employed to collect spectral data from eggplant seeds, covering 360 bands within a wavelength range of 395.24–1008.20 nm. …”
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PVDF Piezoelectric Sensors for Seeds Counting and Coulter Clogging Detection in Sowing Process Monitoring by Łukasz Gierz, Bartłomiej Kazimierz Paszkiewicz

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Preliminary Research on Intelligent Baking Room Dehydration and Drying Technology for Rice Sterile Seeds by Man Luo

“…And the chattering of the sliding mode was eliminated by the adaptive integral sliding mode surface. The seeds of three rice varieties were dried, the drying dehydration rate, seed germination rate, and seed vigor were measured, and the changes of seed moisture and temperature during drying were observed. …”
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DualTransAttNet: A Hybrid Model with a Dual Attention Mechanism for Corn Seed Classification by Fei Pan, Dawei He, Pengjun Xiang, Mengdie Hu, Daizhuang Yang, Fang Huang, Changmeng Peng

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Detection of Water Content of Watermelon Seeds Based on Hyperspectral Reflection Combined with Transmission Imaging by Siyi Ouyang, Siwei Lv, Bin Li

“…In this study, reflectance and transmittance spectral data from hyperspectral imaging were fused to improve the detection accuracy of moisture content in watermelon seeds. First, watermelon seed samples with different water content gradients were prepared by dividing all 456 selected watermelon seeds into 10 groups and drying them in a drying oven at 60 °C for 0, 3, 5, 10, 15, 20, 25, 30, 40, and 50 min. …”
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Machine Learning Inference of Gene Regulatory Networks in Developing <i>Mimulus</i> Seeds by Albert Tucci, Miguel A. Flores-Vergara, Robert G. Franks

“…We deployed two GRN inference algorithms—RTP-STAR and KBoost—on three different subsets of our transcriptomic dataset. …”
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An effective and efficient hierarchical -means clustering algorithm by Jianpeng Qi, Yanwei Yu, Lihong Wang, Jinglei Liu, Yingjie Wang

“…However, k -means often becomes sensitive due to its random seeds selecting. Motivated by this, this article proposes an optimized k -means clustering method, named k* -means, along with three optimization principles. …”
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