AIseed: An automated image analysis software for high-throughput phenotyping and quality non-destructive testing of individual plant seeds

考古遗址中农作物种子/果核表型性状研究述评

“…但粟黍种子的形状近似椭圆而非椭圆, 由此计 算出面积的可靠性仍需考虑. 国内学者在研究水稻种 子表型性状时还关注了粒重, 部分研究直接测量粒 重 [24] , 部分研究利用公式计算粒重指数, 如粒重≈(4/ 3•π•LWT/8)×0.865129 [25] SmartGrain [27] 、AIseed [28] [9] , 人类骨骼遗骸的生物学特征重建、古人类 性别、年龄、祖籍与种族评估 [29] , 植物大遗存以及淀 粉粒微体遗存的种属鉴定和分类等 [30,31] . 几何形态测 量法通过采集同源性一致的地标点或轮廓线数据, 获 取研究对象的几何形状信息, 结合多元统计分析方 法、数字图像技术等手段量化形态特征, 实现形态的 可视化 [32] .…”

Crop HTP Technologies: Applications and Prospects

“…In today's era of high-throughput technology, many researchers are developing various system models and methods for the identification and prediction of various plant fruits (seeds) before maturation, as illustrated in Table 3. The process of fruit/seed identification and prediction mainly involves the following steps: obtaining seed images, image processing and feature extraction, importing corresponding analysis tools, measuring using different models for different tasks, training and evaluating the selected models, analyzing data, and obtaining seeds' phenotypic traits [76]. Currently, the high-throughput acquisition of fruit/seed phenotypic information is concentrated on researching seed number, purity, vitality, etc.…”

An RGB image dataset for seed germination prediction and vigor detection - maize

“…The advancement of seed vigor detection technology has raised the bar for modern agriculture. The hotspot and trend of current mainstream research is machine learning-based detection technology, which is a non-contact direct measuring method with the benefits of being direct, quick, true, and dependable ( Medeiros et al., 2020 ; Wen-ling et al., 2020 ; Sun et al, 2021 ; Tu et al., 2023 ).By using RGB to obtain corn seed images, the authors combined HSI and 3DCNN to establish an optimal classified corn seed vitality model ( Fan et al., 2023 ). In farming, measuring seed vigor is crucial, and a non-destructive machine vision method for detecting seed vigor can aid in a more accurate assessment of seed quality.…”

“…In farming, measuring seed vigor is crucial, and a non-destructive machine vision method for detecting seed vigor can aid in a more accurate assessment of seed quality. This provides seed companies with a better basis for decision-making when selecting cultivars and managing plantings ( Yasmin et al., 2019 ; Tu et al., 2023 ). The digital image of soybean was obtained by using RGB, and the character of soybean was evaluated automatically by using Python Algorithm ( Ghimire et al., 2023 ).…”

“…High-vigor seeds are a crucial assurance of successful harvests and higher agricultural product yields since they have apparent growth advantages and output potential (Riveiro et al, 2020). The International Association of Seed Testing (IAST) recommends several methods for determining seed vigor, including germination, cold resistance, accelerated aging, conductivity, and enzyme activity (Fenollosa et al, 2020;Ma et al, 2020;Ali et al, 2022;Zhang et al, 2023). Traditional vigor testing techniques have drawbacks including lengthy measurement times, heavy seed usage, subpar measurement accuracy, and low sensitivity (Peng et al, 2018;Zhu et al, 2019;Pang et al, 2020, Pang et al, 2021.…”