Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： Aiming at the difficulty of accurately extract the phenotypic traits of plants and organs from images or point clouds caused by the multiple tillers and serious cross-occlusion among organs of wheat plants, to meet the needs of accurate phenotypic analysis of wheat plants, three-dimensional (3D) digitization was used to extract phenotypic parameters of wheat plants. Firstly, digital representation method of wheat organs was given and a 3D digital data acquisition standard suitable for the whole growth period of wheat was formulated. According to this standard, data acquisition was carried out using a 3D digitizer. Based on the definition of phenotypic parameters and semantic coordinates information contained in the 3D digitizing data, eleven conventional measurable phenotypic parameters in three categories were quantitative extracted, including lengths, thicknesses, and angles of wheat plants and organs. Furthermore, two types of new parameters for shoot architecture and 3D leaf shape were defined. Plant girth was defined to quantitatively describe the looseness or compactness by fitting 3D discrete coordinates based on the least square method. For leaf shape, wheat leaf curling and twisting were defined and quantified according to the direction change of leaf surface normal vector. Three wheat cultivars including FK13, XN979, and JM44 at three stages (rising stage, jointing stage, and heading stage) were used for method validation. The Open3D library was used to process and visualize wheat plant data. Visualization results showed that the acquired 3D digitization data of maize plants were realistic, and the data acquisition approach was capable to present morphological differences among different cultivars and growth stages. Validation results showed that the errors of stem length, leaf length, stem thickness, stem and leaf angle were relatively small. The R2 were 0.93, 0.98, 0.93, and 0.85, respectively. The error of the leaf width and leaf inclination angle were also satisfactory, the R2 were 0.75 and 0.73. Because wheat leaves are narrow and easy to curl, and some of the leaves have a large degree of bending, the error of leaf width and leaf angle were relatively larger than other parameters. The data acquisition procedure was rather time-consuming, while the data processing was quite efficient. It took around 133 ms to extract all mentioned parameters for a wheat plant containing 7 tillers and total 27 leaves. The proposed method could achieve convenient and accurate extraction of wheat phenotypes at individual plant and organ levels, and provide technical support for wheat shoot architecture related research.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： Apple mouldy core disease often occurs in the ventricle of apples and cannot be effectively identified by appearance. Near-infrared spectroscopy, nuclear magnetic resonance and other methods are usually used in traditional apple mouldy core disease detection, but these methods require complex equipment and high detection costs. In this research, a simple and fast nondestructive detection method of apple mouldy core disease was proposed by using a dielectric method to construct an apple mouldy core disease detection model. Japan's Hioki 3532-50 LCR tester was used to collect 108 dielectric indicators (12 dielectric indicators at 9 frequencies) of 220 apples as the original data. Due to the large differences in the distribution of data collected with different dielectric indexes and different frequencies, a standardized method was used for data preprocessing to eliminate the problem of large differences in dielectric data distribution. Afterwards, in order to eliminate the redundant information between the data, the principal component analysis algorithm was used to reduce the data dimensionality, and finally the three algorithms of BP neural network (BPNN), support vector machine (SVM) and random forest (RF) were used to construct the mouldy core disease detection model. After pre-experiment, the most effective parameters of each algorithm were selected, the test results showed that the apple mouldy core disease detection model based on the RF algorithm obtained the best performance, and the detection accuracy rate reached 96.66% and 95.71% in the training set (150 apples) and the test set (70 apples). The mouldy core disease detection model constructed by using BPNN was the second most effective, and the detection accuracy could reach 94.66% and 94.29%, respectively. The detection effect of the model built by using SVM was relatively poor, and the detection accuracies were 93.33% and 91.43%, respectively. The experimental results showed that the model constructed by using RF can more effectively identify mouldy core disease apples and healthy apples. This study could provide references for apple diseases and insect pests and non-destructive testing of apple quality.
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