A Recognition Method of Soybean Leaf Diseases Based on an Improved Deep Learning Model.

Soybean is an important oil crop and plant protein source, and phenotypic traits' detection for soybean diseases, which seriously restrict yield and quality, is of great significance for soybean breeding, cultivation, and fine management. The recognition accuracy of traditional deep learning models is not high, and the chemical analysis operation process of soybean diseases is time-consuming. In addition, artificial observation and experience judgment are easily affected by subjective factors and difficult to guarantee the accuracy of the objective. Thus, a rapid identification method of soybean diseases was proposed based on a new residual attention network (RANet) model. First, soybean brown leaf spot, soybean frogeye leaf spot, and soybean phyllosticta leaf spot were used as research objects, the OTSU algorithm was adopted to remove the background from the original image. Then, the sample dataset of soybean disease images was expanded by image enhancement technology based on a single leaf image of soybean disease. In addition, a residual attention layer (RAL) was constructed using attention mechanisms and shortcut connections, which further embedded into the residual neural network 18 (ResNet18) model. Finally, a new model of RANet for recognition of soybean diseases was established based on attention mechanism and idea of residuals. The result showed that the average recognition accuracy of soybean leaf diseases was 98.49%, and the F1-value was 98.52 with recognition time of 0.0514 s, which realized an accurate, fast, and efficient recognition model for soybean leaf diseases.

Rapid detection method of Pleurotus eryngii mycelium based on near infrared spectral characteristics.

Edible fungus is a large fungus with edible and medicinal value. Rapid detection of mycelium phenotypic characteristics is of great significance for edible fungus breeding and intelligent cultivation. Traditional method based on experienced observation easily led to make mistakes on distinguishing the growth stages, which impacted on the yield and quality of edible fungus. Therefore, in view of the lack of accurate and efficient detection technology during the growth stages of Pleurotus eryngii mycelium, a rapid detection method of Pleurotus eryngii mycelium at different growth stages is proposed based on the characteristics of near-infrared spectroscopy. First, the spectral data of mycelium of Pleurotus eryngii at six different growth stages were scanned. Second, the multivariate scattering correction method (MSC) was used to pre-process the raw spectral data, and then the competitive adaptive reweighted sampling algorithm (CARS) was adopted to detect the characteristic wave number of the effective variables for Pleurotus eryngii mycelium. In addition, the mathematical model between the mycelium of Pleurotus eryngii and the characteristic wave number of near-infrared spectrum was established by using feed forward neural network (BP). Finally, and the coding vector output by the network was used to detect to the growth stages. The results showed that the BP neural network structure of MSC-CARS-BP detection model was 86-85-85-95-6, and the accuracy of identifying different growth stages of Pleurotus eryngii mycelium was 99.67%. The research results could provide a new idea and technical support for the rapid detection of Pleurotus eryngii mycelium at different growth stages.

Recognition of soybean pods and yield prediction based on improved deep learning model.

As a leaf homologous organ, soybean pods are an essential factor in determining yield and quality of the grain. In this study, a recognition method of soybean pods and estimation of pods weight per plant were proposed based on improved YOLOv5 model. First, the YOLOv5 model was improved by using the coordinate attention (CA) module and the regression loss function of boundary box to detect and accurately count the pod targets on the living plants. Then, the prediction model was established to reliably estimate the yield of the whole soybean plant based on back propagation (BP) neural network with the topological structure of 5-120-1. Finally, compared with the traditional YOLOv5 model, the calculation and parameters of the proposed model were reduced by 17% and 7.6%, respectively. The results showed that the average precision (AP) value of the improved YOLOv5 model reached 91.7% with detection rate of 24.39 frames per millisecond. The mean square error (MSE) of the estimation for single pod weight was 0.00865, and the average coefficients of determination R2 between predicted and actual weight of a single pod was 0.945. The mean relative error (MRE) of the total weight estimation for all potted soybean plant was 0.122. The proposed method can provide technical support for not only the research and development of the pod's real-time detection system, but also the intelligent breeding and yield estimation.

Calculation Method for Phenotypic Traits Based on the 3D Reconstruction of Maize Canopies.

A reasonable plant type is an essential factor for improving canopy structure, ensuring a reasonable expansion of the leaf area index and obtaining a high-quality spatial distribution of light. It is of great significance in promoting effective selection of the ecological breeding index and production practices for maize. In this study, a method for calculating the phenotypic traits of the maize canopy in three-dimensional (3D) space was proposed, focusing on the problems existing in traditional measurement methods in maize morphological structure research, such as their complex procedures and relatively large error margins. Specifically, the whole maize plant was first scanned with a FastSCAN hand-held scanner to obtain 3D point cloud data for maize. Subsequently, the raw point clouds were simplified by the grid method, and the effect of noise on the quality of the point clouds in maize canopies was further denoised by bilateral filtering. In the last step, the 3D structure of the maize canopy was reconstructed. In accordance with the 3D reconstruction of the maize canopy, the phenotypic traits of the maize canopy, such as plant height, stem diameter and canopy breadth, were calculated by means of a fitting sphere and a fitting cylinder. Thereafter, multiple regression analysis was carried out, focusing on the calculated data and the actual measured data to verify the accuracy of the calculation method proposed in this study. The corresponding results showed that the calculated values of plant height, stem diameter and plant width based on 3D scanning were highly correlated with the actual measured data, and the determinant coefficients R² were 0.9807, 0.8907 and 0.9562, respectively. In summary, the method proposed in this study can accurately measure the phenotypic traits of maize. Significantly, these research findings provide technical support for further research on the phenotypic traits of other crops and on variety breeding.