2023
DOI: 10.3389/fpls.2023.1127108
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Detection of peanut seed vigor based on hyperspectral imaging and chemometrics

Abstract: Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselve… Show more

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Cited by 8 publications
(6 citation statements)
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References 51 publications
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“…Next research proposed a lane detection method based on Mask R-CNN, achieving accurate lane boundary extraction in challenging scenarios such as low-light conditions and occlusions [23]. Similarly, [24] utilized Mask R-CNN for lane detection in urban environments, demonstrating robust performance in complex traffic scenes. These studies highlight the effectiveness of Mask R-CNN in handling real-world challenges encountered in autonomous driving scenarios.…”
Section: Related Workmentioning
confidence: 99%
“…Next research proposed a lane detection method based on Mask R-CNN, achieving accurate lane boundary extraction in challenging scenarios such as low-light conditions and occlusions [23]. Similarly, [24] utilized Mask R-CNN for lane detection in urban environments, demonstrating robust performance in complex traffic scenes. These studies highlight the effectiveness of Mask R-CNN in handling real-world challenges encountered in autonomous driving scenarios.…”
Section: Related Workmentioning
confidence: 99%
“…This versatile machine learning framework boasts a crucial advantage in its ability to effectively manage categorical variables with high cardinality and handle missing data, making it a valuable tool for real-world datasets (Dorogush et al, 2018). When combined with HSI, it offers an effective approach for accurately predicting the content of various sample components, particularly in the food industry, such as fat, protein, and moisture (Zou et al, 2023).…”
Section: Conventional Data Analysis Approachesmentioning
confidence: 99%
“…Meeting this demand sustainably necessitates solutions that not only bolster agricultural productivity in terms of land use but also optimize resource allocation. One effective strategy to enhance productivity involves the cultivation of high-vigor seeds [2][3][4]. The quality of these seeds, influenced by genetic, physical, sanitary, and physiological factors, profoundly impacts crop development, thereby directly affecting yield potential across plant species [5,6].…”
Section: Introductionmentioning
confidence: 99%
“…Moreover, recent advancements in computer vision, machine learning, and deep learning have gained considerable attention for seed quality evaluation [4,14,16]. Convolutional neural networks (CNNs) have shown particular promise for image-based seed assessment by learning complex patterns from seed images which are difficult to discern by traditional methods [11,14,16].…”
Section: Introductionmentioning
confidence: 99%