Corn seed materials of different quality were imaged, and a method for defect detection was developed based on a watershed algorithm combined with a two-pathway convolutional neural network (CNN) model. In this study, RGB and near-infrared (NIR) images were acquired with a multispectral camera to train the model, which was proved to be effective in identifying defective seeds and defect-free seeds, with an averaged accuracy of 95.63%, an averaged recall rate of 95.29%, and an F1 (harmonic average evaluation) of 95.46%. Our proposed method was superior to the traditional method that employs a one-pathway CNN with 3-channel RGB images. At the same time, the influence of different parameter settings on the model training was studied. Finally, the application of the object detection method in corn seed defect detection, which may provide an effective tool for high-throughput quality control of corn seeds, was discussed.

The phenotypic parameters of crop plants can be evaluated accurately and quickly using an unmanned aerial vehicle (UAV) equipped with imaging equipment. In this study, hundreds of images of Chinese cabbage (Brassica rapa L. ssp. pekinensis) germplasm resources were collected with a low-cost UAV system and used to estimate cabbage width, length, and relative chlorophyll content (soil plant analysis development [SPAD] value). The super-resolution generative adversarial network (SRGAN) was used to improve the resolution of the original image, and the semantic segmentation network Unity Networking (UNet) was used to process images for the segmentation of each individual Chinese cabbage. Finally, the actual length and width were calculated on the basis of the pixel value of the individual cabbage and the ground sampling distance. The SPAD value of Chinese cabbage was also analyzed on the basis of an RGB image of a single cabbage after background removal. After comparison of various models, the model in which visible images were enhanced with SRGAN showed the best performance. With the validation set and the UNet model, the segmentation accuracy was 94.43%. For Chinese cabbage dimensions, the model was better at estimating length than width. The R² of the visible-band model with images enhanced using SRGAN was greater than 0.84. For SPAD prediction, the R² of the model with images enhanced with SRGAN was greater than 0.78. The root mean square errors of the 3 semantic segmentation network models were all less than 2.18. The results showed that the width, length, and SPAD value of Chinese cabbage predicted using UAV imaging were comparable to those obtained from manual measurements in the field. Overall, this research demonstrates not only that UAVs are useful for acquiring quantitative phenotypic data on Chinese cabbage but also that a regression model can provide reliable SPAD predictions. This approach offers a reliable and convenient phenotyping tool for the investigation of Chinese cabbage breeding traits.

Current inspections of seed appearance quality are mainly performed manually, which is time-consuming, tedious, and subjective, and creates difficulties in meeting the needs of practical applications. For rapid and accurate identification of seeds based on appearance quality, this study proposed a seed-quality evaluation method that used an improved Inception-ResNet network with corn seeds of different qualities. First, images of multiple corn seeds were segmented to build a single seed image database. Second, the standard convolution of the Inception-ResNet module was replaced by a depthwise separable convolution to reduce the number of model parameters and computational complexity of the network. In addition, an attention mechanism was applied to improve the feature learning performance of the network model and extract the best image information to express the appearance quality. Finally, the feature fusion strategy was used to fuse the feature information at different levels to prevent the loss of important information. The results showed that the proposed method had decent comprehensive performance in detection of corn seed appearance quality, with an average of 96.03% for detection accuracy, 96.27% for precision, 96.03% for recall rate, 96.15% for F1 value of reconciliation, and the average detection time of an image was about 2.44 seconds. This study realized rapid nondestructive detection of seeds and provided a theoretical basis and technical support for construction of intelligent seed sorting equipment.

In this study, eggplant seeds of fifteen different varieties were selected for discriminant analyses with a multispectral imaging technique. Seventy‐eight features acquired with the multispectral images were extracted from individual eggplant seeds, which were then classified using SVM and a one‐dimensional convolutional neural network (1D‐CNN), and the overall accuracy was 90.12% and 94.80%, respectively. A two‐dimensional convolutional neural network (2D‐CNN) was also adopted for discrimination of seed varieties, and an accuracy of 90.67% was achieved. This study not only demonstrated that multispectral imaging combining machine learning techniques could be used as a high‐throughput and nondestructive tool to discriminate seed varieties but also revealed that the shape of the seed shell may not be exactly the same as the female parents due to the genetic and environmental factors.

Soluble sugar is an important index to determine the quality of jujube, and also an important factor to influence the taste of jujube. The acquisition of the soluble sugar content of jujube mainly relies on manual chemical measurement which is time-consuming and labor-intensive. In this study, the feasibility of multispectral imaging combined with deep learning for rapid nondestructive testing of fruit internal quality was analyzed. Support vector machine regression model, partial least squares regression model, and convolutional neural networks (CNNs) model were established by multispectral imaging method to predict the soluble sugar content of the whole jujube fruit, and the optimal model was selected to predict the content of three kinds of soluble sugar. The study showed that the sucrose prediction model of the whole jujube had the best performance after CNNs training, and the correlation coefficient of verification set was 0.88, which proved the feasibility of using CNNs for prediction of the soluble sugar content of jujube fruits.

Clubroot, caused by Plasmodiophora brassicae, is a destructive disease of Chinese cabbage (Brassica rapa ssp. pekinensis) at all growing stages. Early detection of the disease is essential to mitigate the impact of clubroot. Here, we established an optimal algorithm for multispectral imaging combined with machine learning to detect leaf responses of highly susceptible cultivar YoulvNo.3 at different day after inoculation (DAI). Spectral data at 19 wavelengths were collected from leaf multispectral images, and key characteristic wavelengths were further extracted. Principal Component Analysis (PCA) revealed a clear separation between healthy and infected samples at 11 DAI. Four classification algorithms, including Random Forest (RF), Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM) and Extreme Learning Machine (ELM), were employed to construct early detection model for clubroot. SVM achieved over 81% accuracy with full-spectrum data, while ELM based on characteristic wavelengths provided the best performance, accuracy exceeding 84%. Stratified five-fold cross-validation was used to validate the optimal model. An average accuracy of 83.79% (±1.04%) and macro-averaged F1-score of 82.13% (±1.12%) across validation folds were obtained, confirming stable performance. Our findings, for the first time, identified detectable spectral differences between the healthy and infected plants at 11 DAI using leaf multispectral combined with machine learning, providing a potential application for early detection of clubroot and timely control in Chinese cabbage.