Strawberry grading by picking robots can eliminate the manual classification, reducing labor costs and minimizing the damage to the fruit. Strawberry size or weight is a key factor in grading, with accurate weight estimation being crucial for proper classification. In this paper, we collected 1521 sets of strawberry RGB-D images using a depth camera and manually measured the weight and size of the strawberries to construct a training dataset for the strawberry weight regression model. To address the issue of incomplete depth images caused by environmental interference with depth cameras, this study proposes a multimodal point cloud completion method specifically designed for symmetrical objects, leveraging RGB images to guide the completion of depth images in the same scene. The method follows a process of locating strawberry pixel regions, calculating centroid coordinates, determining the symmetry axis via PCA, and completing the depth image. Based on this approach, a multimodal fusion regression model for strawberry weight estimation, named MMF-Net, is developed. The model uses the completed point cloud and RGB image as inputs, and extracts features from the RGB image and point cloud by EfficientNet and PointNet, respectively. These features are then integrated at the feature level through gradient blending, realizing the combination of the strengths of both modalities. Using the Percent Correct Weight (PCW) metric as the evaluation standard, this study compares the performance of four traditional machine learning methods, Support Vector Regression (SVR), Multilayer Perceptron (MLP), Linear Regression, and Random Forest Regression, with four point cloud-based deep learning models, PointNet, PointNet++, PointMLP, and Point Cloud Transformer, as well as an image-based deep learning model, EfficientNet and ResNet, on single-modal datasets. The results indicate that among traditional machine learning methods, the SVR model achieved the best performance with an accuracy of 77.7% (PCW@0.2). Among deep learning methods, the image-based EfficientNet model obtained the highest accuracy, reaching 85% (PCW@0.2), while the PointNet + + model demonstrated the best performance among point cloud-based models, with an accuracy of 54.3% (PCW@0.2). The proposed multimodal fusion model, MMF-Net, achieved an accuracy of 87.66% (PCW@0.2), significantly outperforming both traditional machine learning methods and single-modal deep learning models in terms of precision.

In recent years, the data denoising, as an indispensable part of sensor data processing, has been a focus in signal processing community. Aquaculture water quality data is the basis of real-time monitoring, data query and analysis, water quality forecasting and early warning, and remote control of aquaculture management system. However, when collecting and transiting these sensor data, they will be polluted to a certain degree, which will affect the subsequent processing of the data. Wavelet is a feasible denoising method for sensor data, but output signal is delayed and not real-time. The paper proposes a real-time denoising model WAVELET-RNN which is based on wavelet denoising and recurrent neural network (RNN). The experiment results show that the model can effectively remove the noise in water quality sensor data. In addition, the model can easily fuse multiple sensor data to improve the denoising performance by exploiting the correlation between them.

Sugar content is a crucial indicator of grape ripeness and grading, and developing non-contact and non-destructive sugar content detection devices is essential for grape-picking robots and sorting platforms. Spectroscopy, which can detect the chemical composition of grapes, has become a key technology for developing non-destructive testing devices. In this paper, we collected 2,880 randomly labeled multispectral images of Sunshine Rose grapes with a Changguang Yuchen MS600 PRO multispectral camera and measured the sugar content (in Brix values) of the labeled grapes with a handheld refractometer, using data exclusively from this grape variety. To address noise and misalignment issues in the multispectral images, we proposed preprocessing methods including Gaussian denoising and ECC (Enhanced Correlation Coefficient) algorithm registration. Based on a ResNet-50 residual network, we constructed a grape sugar content prediction regression model Improved-Res with SE (Squeeze-and-Excitation) attention modules, DSC (Depthwise Separable Convolutions), and Inception modules. The model’s performance was evaluated by MSE (Mean Squared Error), MAE (Mean Absolute Error), and R² (R-Square) metrics. We compared the performance of four feature extraction methods combined with four traditional machine learning models, as well as seven deep learning models. The results showed that among traditional machine learning methods, the combination of color histogram feature extraction and the XGBoost regression achieved the best performance, with MSE, MAE, and R² of 1.35, 0.90 Brix, and 0.78, respectively. Among deep learning methods, the ResNet-50 model demonstrated the best performance, with MSE, MAE, and R² of 0.95, 0.96 Brix, and 0.84, respectively. Effective improvements of SE attention module, depthwise separable convolutions, and Inception module in the ResNet-50 model was confirmed through ablation experiments: the proposed Improved-Res model achieved MSE, MAE, and R² of 0.49, 0.55 Brix, and 0.92, respectively, which significantly outperformed traditional machine learning methods and classical deep learning models.

Multi-face alignment aims to identify geometry structures of multiple faces in an image, and its performance is essential for the many practical tasks, such as face recognition, face tracking, and face animation. In this work, we present a fast bottom-up multi-face alignment approach, which can simultaneously localize multi-person facial landmarks with high precision.In more detail, our bottom-up architecture maps the landmarks to the high-dimensional space with which landmarks of all faces are represented. By clustering the features belonging to the same face, our approach can align the multi-person facial landmarks synchronously.Extensive experiments show that our method can achieve high performance in the multi-face landmark alignment task while our model is extremely fast. Moreover, we propose a new multi-face dataset to compare the speed and precision of bottom-up face alignment method with top-down methods. Our dataset is publicly available at https://github.com/AISAResearch/FoxNet