Macadamia leaf structure characteristic database, variety identification method, breeding method and application

Abstract

The invention provides a macadimia nut leaf structure characteristic database, a variety identification and breeding method and application, and belongs to the technical field of plant variety identification and breeding. According to the method, leaf structure features are utilized, and a macadamia leaf structure feature database is constructed by collecting leaf samples and measuring multiple core indexes such as leaf length, the number of stomata and the number of veins; the method is simple, the operation is simple and convenient, and complex equipment is not needed. Moreover, the method is high in accuracy, the leaf structure characteristics are subjected to genetic control, the environmental interference is small, and the identification accuracy can reach 92% or above. The macadimia nut leaf structure feature database constructed by the method provides an efficient solution for variety breeding, the breeding efficiency is improved by 40%, and the breeding period is shortened by 2-3 years. In addition, the technical achievement of the invention can be directly applied to seedling breeding, variety right protection and breeding practice, and has a wide industrialization prospect.

Description

Technical Field

[0001] This invention relates to the field of plant variety identification and breeding technology, and in particular to a database of macadamia leaf structural characteristics, variety identification, breeding methods and applications. Background Technology

[0002] Macadamia (Macadamia integrifolia), a globally favored high-end nut crop, boasts abundant unsaturated fatty acids, high-quality protein, and various minerals in its fruit. It not only holds a significant position in the food processing industry but has also become a core industry driving agricultural economic development in many regions, demonstrating substantial economic value. In recent years, with the continuous increase in global market demand for macadamia nuts, its planting area has expanded rapidly. However, behind the ever-expanding industry scale, problems such as mixed varieties and uneven quality have become increasingly prominent, severely restricting the improvement of quality and efficiency in the macadamia nut industry and becoming a bottleneck that urgently needs to be overcome for the current industry development.

[0003] Traditional methods for identifying macadamia varieties primarily rely on macroscopic characteristics such as fruit morphology (e.g., fruit size, shell thickness, shape index) and plant growth habits (e.g., plant compactness, flowering time, branch angle). While this method played a role in the early stages of industry development, its limitations have become increasingly apparent with the increase in the number of varieties and the emergence of closely related species. From a subjective perspective, traditional methods depend entirely on manual observation and judgment. However, macadamia growth is easily affected by environmental factors such as light intensity, water and fertilizer supply, and soil pH, leading to significant differences in fruit morphology and growth habits for the same variety under different growing conditions, thus causing serious deviations in the identification results. In terms of accuracy, the macroscopic characteristics of different macadamia nut varieties overlap significantly. For example, some closely related varieties show only slight differences in fruit size and shell thickness, making them difficult to distinguish precisely with the naked eye. Furthermore, the plant type and flowering period of some varieties change at different growth stages, further increasing the difficulty of identification and frequently leading to misjudgments. This causes considerable trouble for variety breeding, planting promotion, and market transactions. In addition, traditional methods suffer from the significant drawback of a long time cycle. Since fruit characteristics can only be observed after the plant has matured and produced fruit, and macadamia nuts typically take 3-5 years from planting to their first fruiting, this severely delays variety identification and breeding, hindering the promotion and application of superior varieties and failing to meet the urgent demand for high-quality varieties in the rapidly developing industry.

[0004] As the core organ for photosynthesis and nutrient transport in plants, the structural characteristics of leaves (such as length, stomatal distribution, and vein density) are determined by genetic material, possessing advantages such as high stability, quantifiability, and early observability. Studies have shown that stomatal density is positively correlated with photosynthetic efficiency, and vein distribution affects nutrient transport capacity; these characteristics are closely related to agronomic traits such as yield and stress resistance.

[0005] In-depth research into the structural characteristics of macadamia leaves and the development of variety identification and breeding technologies based on these characteristics can effectively address the current challenges in variety identification within the industry, such as high subjectivity, low accuracy, and long cycles. This will enable rapid and precise identification of macadamia varieties and provide a scientific and effective technical means for breeding superior varieties. It will help select high-yielding, stress-resistant, and high-quality varieties, promoting the standardization and efficiency of the macadamia industry. The development and application of this technology will further regulate the macadamia variety market, improve the overall production level of the industry, and enhance the competitiveness of macadamia in the international market. This has significant practical and far-reaching strategic implications for promoting the sustainable and healthy development of the global macadamia industry. Summary of the Invention

[0006] The purpose of this invention is to provide a database of macadamia leaf structure characteristics, a method for variety identification and breeding, and its application. By establishing a database of leaf structure characteristics, it is possible to quickly identify varieties and conduct targeted screening of varieties with superior traits. The operation is simple and highly accurate.

[0007] To achieve the above-mentioned objectives, the present invention provides the following technical solution: This invention provides a database of macadamia leaf structural features, characterized in that the data in the database includes various macadamia varieties and their corresponding leaf tissue length data, stomatal number data, leaf vein number data, stomatal density data, and leaf vein number per unit length data.

[0008] Preferably, the method for constructing the macadamia leaf structural feature database includes the following steps: (1) Data standardization: The measured values ​​of leaf tissue length, stomatal number, leaf vein number, stomatal density, and leaf vein number per unit length of each variety are statistically analyzed, and the average value and standard deviation are calculated to form a “variety-feature vector” dataset. (2) Database structure: The “variety-feature vector” dataset is stored in a relational database. The fields include variety name, sample source, leaf tissue length, number of stomata, number of leaf veins, stomatal density, number of leaf veins per unit length and data collection time, to obtain the macadamia leaf structure feature database. (3) Dynamic updates: Regularly include data on newly bred varieties or wild closely related species.

[0009] This invention also provides a method for identifying macadamia varieties based on the aforementioned macadamia leaf structure characteristic database, comprising the following steps: (1) Statistical analysis was conducted on the tissue length data, stomatal number data, leaf vein number data, stomatal density data, and leaf vein number per unit length of macadamia nut leaves. The mean and standard deviation were calculated, and a database of macadamia nut leaf structural characteristics was established. (2) Measure the leaf tissue length, number of stomata, number of veins, stomatal density, and number of veins per unit length of the sample to be identified to obtain the feature vector of the sample to be identified; (3) Use Euclidean distance to measure the difference between the feature vectors of the sample to be identified and the corresponding varieties in the database, or perform cluster analysis to classify the sample to be identified into the nearest variety cluster.

[0010] Preferably, if the Euclidean distance is <0.5 or the cluster confidence level is >90%, it is determined to be the variety; if the distance is >1.0 or the confidence level is <70%, it is marked as "unmatched variety".

[0011] The present invention also provides an application of the above-mentioned method for identifying macadamia varieties in the identification of macadamia varieties.

[0012] This invention also provides a method for breeding superior macadamia varieties based on the aforementioned macadamia leaf structure characteristic database. The screening indicators for this method are as follows: (1) High photosynthetic potential: stomatal density SD > 1.0 n / mm; (2) High-efficiency transport capacity: number of veins per unit length, VL > 2.8 veins / mm; (3) Comprehensive indicators: The blade length LL is 10.5-12.0 mm; (4) Field verification: Conduct field trials for more than 3 years on the initially selected varieties, measure agronomic traits such as yield per plant, kernel yield, and kernel oil content, and combine with stress resistance assessment to finally determine the superior varieties suitable for promotion; Macadamia nut varieties that meet all of the above criteria are considered superior varieties.

[0013] This invention also provides an application of a method for breeding superior macadamia varieties in the breeding of superior macadamia varieties.

[0014] The beneficial effects of this invention compared to the prior art are as follows: (1) This invention utilizes leaf structural characteristics, collects leaf samples, and measures multiple core indicators such as leaf length, number of stomata, and number of veins to construct a database of macadamia leaf structural characteristics. Then, it uses Euclidean distance or cluster analysis to determine the variety classification of the samples. The method is simple, easy to operate, requires no complex equipment, and can be completed in a basic laboratory (such as with an optical microscope and calipers), reducing costs by more than 50%. Furthermore, this invention has high accuracy; leaf structural characteristics are genetically controlled, have minimal environmental interference, and achieve an identification accuracy rate of over 92%.

[0015] (2) Based on the constructed macadamia leaf structure characteristic database, this invention provides an efficient solution for variety selection, improving selection efficiency by 40%, allowing selection to be carried out during the seedling stage, and shortening the breeding cycle by 2-3 years. Furthermore, the technical achievements of this invention can be directly applied to seedling propagation, variety rights protection, and breeding practices, and have broad industrialization prospects. Detailed Implementation

[0016] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0017] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0018] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0019] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0020] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0021] Example 1 Embodiment 1 of the present invention provides a method for identifying macadamia nut varieties, the specific steps of which are as follows: 1. Sample screening and collection Select mature macadamia trees of different varieties with an age of ≥5 years or 1-2 year old seedlings. Collect mature leaves from the middle of the current year's branches of the plant, which are free from diseases and pests and have consistent light conditions. Collect 15-20 leaves from each variety to ensure the representativeness of the sample.

[0022] The following five core indicators were measured on the collected leaf samples. The specific methods and parameter ranges are as follows: 2. Measurement of blade structural characteristics a. Leaf tissue length (LL, mm) Measuring tool: Electronic vernier caliper with an accuracy of 0.01 mm; Measurement method: The straight-line distance from the base (where the petiole connects to the leaf blade) to the leaf tip along the direction of the main vein; Data range: Based on experimental verification, the LL range for different varieties is 9.44 mm to 13.1 mm.

[0023] b. Number of pores (SN, pores / mm) 2 ) Measurement area: middle part of the lower epidermis of the leaf (avoiding the midrib), observed using scanning electron microscopy; Statistical method: at 1mm 2 Three repeated regions were randomly selected within the field of view, the total number of stomata was counted, and the average value was taken. Data range: SN ranges from 4 to 20 per mm for different varieties. 2 .

[0024] c. Number of leaf veins (VN, veins) Measurement method: After transparent treatment (soaking in 50% ethanol for 24 hours), the total number of main veins and first-order lateral veins (lateral veins refer to branches that directly originate from the main vein) is counted. Data range: 24 to 41 data points for different varieties of VN.

[0025] d. Pore density (SD, pores / mm) Calculation method: SD = SN / LL; Biological significance: It reflects the gas exchange capacity per unit leaf length and is related to transpiration efficiency and photosynthetic rate; Data range: SD range for different varieties is 0.4119 n / mm to 1.6949 n / mm.

[0026] e. Number of veins per unit length (VL, veins / mm) Calculation method: VL = VN / LL; Biological significance: It characterizes the distribution efficiency of leaf veins in the leaf and is positively correlated with the ability to transport water and nutrients; Data range: VL range for different varieties is 1.9048 lines / mm to 3.5965 lines / mm.

[0027] 3. Construction of Variety Characteristic Database Data standardization: The five indicator measurements (LL, SN, VN, SD, VL) of each variety are statistically analyzed, and the mean (Mean) and standard deviation (SD) are calculated to form a "variety-feature vector" dataset; Database structure: A relational database (such as MySQL) is used for storage. Fields include variety name, sample source, LL (Mean±SD), SN (Mean±SD), VN (Mean±SD), SD (Mean±SD), VL (Mean±SD), and data collection time, resulting in a macadamia nut variety characteristic database. Dynamic updates: Data on newly bred varieties or wild closely related species are regularly included.

[0028] 4. Variety identification The leaf samples to be identified were collected and five structural features were measured according to steps 1 and 2. The Euclidean distance is used to measure the characteristic differences between the sample to be identified and the varieties in the database. The formula is: D(x,y)=

[0029] Where, x i Let y be the characteristic value of the sample to be identified. i This represents the average value of the variety characteristics in the database; Statistical analysis: Perform cluster analysis (K-means) to classify the samples to be identified into the nearest variety cluster.

[0030] Identification criteria: If the Euclidean distance is <0.5 or the cluster confidence level is >90%, it is identified as this variety; if the distance is >1.0 or the confidence level is <70%, it is marked as "unmatched variety".

[0031] Example 2 Example 2 of the present invention provides a method for identifying macadamia nut varieties, referring to Example 1. The specific steps are as follows: Following the steps in Example 1, the leaf structure characteristics of three varieties, “Guire No. 1”, “OC”, and “HAES 344”, were measured. Fifteen leaves were collected from each variety. After measurement, the average value of the leaf structure characteristics of each variety was calculated, and a macadamia nut variety characteristic database was established. The results are shown in Table 1.

[0032] Table 1. Average values ​​of leaf structure characteristics for each variety

[0033] Meanwhile, measurements were taken of the sample to be identified (unknown variety), and the results are as follows: The blade is 11.0 mm long and has 14 stomata per mm. 2 The leaf has 37 veins, a stomatal density of 1.27 n / mm, and 3.36 veins / mm per unit length.

[0034] Comparing with the macadamia nut variety characteristic database, the Euclidean distance was calculated using the method in step 4 of Example 1 for variety identification. The results were: Gui Re No. 1 (0.32), OC (0.89), HAES 344 (1.56). Therefore, the sample to be identified was determined to be "Gui Re No. 1".

[0035] Example 3 Embodiment 3 of the present invention provides a method for screening superior varieties, the specific steps of which are as follows: Based on the leaf structure characteristics of the three varieties "Gui Re No. 1", "OC" and "HAES 344" in Example 2, superior varieties were selected and bred. The screening indicators are as follows: 1. High photosynthetic potential: stomatal density SD > 1.0 n / mm (improves CO2 absorption efficiency); 2. High-efficiency transport capacity: Number of veins per unit length (VL) > 2.8 veins / mm (enhancing nutrient and water supply); 3. Overall performance indicators: Leaf length (LL) is 10.5-12.0 mm (balancing photosynthetic area and lodging resistance); 4. Field validation: Conduct field trials for more than 3 years on the initially selected varieties, measure agronomic traits such as yield per plant, kernel yield, and kernel oil content, and combine them with the evaluation of stress resistance (such as drought resistance and disease and pest resistance) to finally determine the superior varieties suitable for promotion.

[0036] Based on the above indicators, “Gui Re No. 1” and “OC” were selected from the macadamia nut variety characteristic database of Example 2. After field trials, their average yield per plant over 3 years was 8.2 kg and 7.5 kg, respectively, which were significantly higher than the control variety (HAES344, 6.1 kg), and the kernel yield was increased by 5%-8%.

[0037] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A database of macadamia leaf structural features, characterized in that, The data in the macadamia leaf structure feature database includes data on various macadamia varieties and their corresponding leaf tissue length, stomatal number, vein number, stomatal density, and vein number per unit length.

2. The macadamia leaf structure feature database according to claim 1, characterized in that, The method for constructing the macadamia leaf structural feature database includes the following steps: (1) Data standardization: The measured values ​​of leaf tissue length, stomatal number, leaf vein number, stomatal density, and leaf vein number per unit length of each variety are statistically analyzed, and the average value and standard deviation are calculated to form a "variety-feature vector" dataset. (2) Database structure: The "variety-feature vector" dataset is stored in a relational database. The fields include variety name, sample source, leaf tissue length, number of stomata, number of leaf veins, stomatal density, number of leaf veins per unit length, and data collection time, to obtain the macadamia leaf structure feature database. (3) Dynamic updates: Regularly include data on newly bred varieties or wild closely related species.

3. A method for identifying macadamia varieties based on the macadamia leaf structure characteristic database of claim 1 or 2, characterized in that, Includes the following steps: (1) Statistical analysis was conducted on the tissue length data, stomatal number data, leaf vein number data, stomatal density data, and leaf vein number per unit length of macadamia nut leaves. The mean and standard deviation were calculated, and a database of macadamia nut leaf structural characteristics was established. (2) Measure the leaf tissue length, number of stomata, number of veins, stomatal density, and number of veins per unit length of the sample to be identified to obtain the feature vector of the sample to be identified; (3) Use Euclidean distance to measure the difference between the feature vectors of the sample to be identified and the corresponding varieties in the database, or perform cluster analysis to classify the sample to be identified into the nearest variety cluster.

4. The method for identifying macadamia nut varieties according to claim 3, characterized in that, In step (3), if the Euclidean distance is <0.5 or the cluster confidence is >90%, it is determined to be the variety; if the distance is >1.0 or the confidence is <70%, it is marked as "unmatched variety".

5. The application of the macadamia nut variety identification method according to claim 3 or 4 in the identification of macadamia nut varieties.

6. A method for breeding superior macadamia varieties based on the macadamia leaf structure characteristic database of claim 1 or 2, characterized in that, The screening indicators for the breeding method of superior macadamia nut varieties are as follows: (1) High photosynthetic potential: stomatal density SD > 1.0 n / mm; (2) High-efficiency transport capacity: number of veins per unit length, VL > 2.8 veins / mm; (3) Comprehensive indicators: The blade length LL is 10.5-12.0 mm; (4) Field verification: Conduct field trials for more than 3 years on the initially selected varieties, measure agronomic traits such as yield per plant, kernel yield, and kernel oil content, and combine with stress resistance assessment to finally determine the superior varieties suitable for promotion; Macadamia nut varieties that meet all of the above criteria are considered superior varieties.

7. The application of the method for breeding superior macadamia varieties as described in claim 6 in the breeding of superior macadamia varieties.

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