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ZOU Xuejie, E-mail: zxj15200516360@163.com
Outline
With the development of agricultural science and technology, factory seedling nursery technology has gradually become an important part of modern agricultural production. The introduction of intelligent seeding detection technology significantly improves the efficiency and accuracy of the seedling nursery process and promotes the intelligent and automated process of agricultural production. Intelligent seeding detection is the key to achieving efficient production of factory seedling nursery. Seeding detection technology has gradually changed from photoelectric detection and capacitive detection to visual detection combined with hyperspectral technology and other methods. The applicable objects of photoelectric detection, capacitance detection, visual detection, and hyperspectral detection technology methods are outlined, and the advantages and disadvantages of existing photoelectric, capacitance, and visual detection technologies are summarised. In photoelectric detection researchers generally study three aspects of detection indicators, heavy leakage detection, and improvement of detection accuracy in high dust environments, which are generally applicable to large grain crops such as soybeans, corn, etc. For capacitive detection, the main analysis of capacitance structure on seeding detection, in addition to the optimization aspects of the polar plate structure, algorithm optimization through signal processing to reduce environmental and signal interference, improve detection accuracy and work stability, reducing the error rate. For the visual detection of domestic and international research is mainly aimed at the leakage of seeding, seed flow, seed falling speed seed, detection algorithm model and deep learning algorithm innovation and other directions of research, for the traditional detection technology accuracy is not high, capacitance detection distance is far away from the problem of visual detection error, put forward the development trend of intelligent seeding detection technology for seedlings and nursery seedlings from detecting leakage of seeding to the other seed biochemical properties, and predicting the yield of seedlings and nursery seedlings, and so on. Provide important decision-making basis and reference significance for building the development of intelligent seedling sowing performance detection related technology based on hyperspectral technology.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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