Accurate and efficient tomato detection is one of the key techniques for intelligent automatic picking in the area of precision agriculture. However, under the facility scenario, existing detection algorithms still have challenging problems such as weak feature extraction ability for occlusion conditions and different fruit sizes, low accuracy on edge location, and heavy model parameters. To address these problems, this paper proposed D³-YOLOv10, a lightweight YOLOv10-based detection framework. Initially, a compact dynamic faster network (DyFasterNet) was developed, where multiple adaptive convolution kernels are aggregated to extract local effective features for fruit size adaption. Additionally, the deformable large kernel attention mechanism (D-LKA) was designed for the terminal phase of the neck network by adaptively adjusting the receptive field to focus on irregular tomato deformations and occlusions. Then, to further improve detection boundary accuracy and convergence, a dynamic FM-WIoU regression loss with a scaling factor was proposed. Finally, a knowledge distillation scheme using semantic frequency prompts was developed to optimize the model for lightweight deployment in practical applications. We evaluated the proposed framework using a self-made tomato dataset and designed a two-stage category balancing method based on diffusion models to address the sample class-imbalanced issue. The experimental results demonstrated that the D³-YOLOv10 model achieved an $mA{P}_{0.5}$ of 91.8%, with a substantial reduction of 54.0% in parameters and 64.9% in FLOPs, compared to the benchmark model. Meanwhile, the detection speed of 80.1 FPS more effectively meets the demand for real-time tomato detection. This study can effectively contribute to the advancement of smart agriculture research on the detection of fruit targets.

Multi-task learning has received great interest recently in the area of machine learning. It shows a considerable capacity to jointly learn multiple latent relationships hidden among tasks, and has been widely used in data mining and computer vision problems. In this paper, we propose a new multi-task based collaborative linear regression framework to address the image classification problem, which allows the class-specific and collaboratively shared latent structure components to be explored simultaneously. The proposed framework takes multi-target regression of each class as a task to transfer shared structures among them. To be more efficient and adaptive, the class-wise nonlinear subspace is also learned in this framework to earn inter-class discrimination and model adaptability. The proposed framework provides a unified and flexible perceptiveness for jointly learning the nonlinear projected features and regression parameters. Furthermore, a numerical scheme via iterative alternating optimization is also developed to solve the novel objective function in the proposed framework and guarantee the convergence. Extensive experimental results tested on several datasets demonstrated that our proposed framework outperforms existing competitive methods and achieves consistently high performance.

Recently, sparse representation, which relies on the underlying assumption that samples can be sparsely represented by their labeled neighbors, has been applied with great success to image classification problems. Through sparse representation-based classification (SRC), the label can be assigned with minimum residual between the sample and its synthetic version with class-specific coding, which means that the coding scheme is the most significant factor for classification accuracy. However, conventional SRC-based coding schemes ignore dependency among the samples, which leads to an undesired result that similar samples may be coded into different categories due to quantization sensitivity. To address this problem, in this paper, a novel approach based on self-supervised sparse representation is proposed for image classification. In the proposed approach, the manifold structure of samples is firstly exploited with low rank representation. Next, the low-rank representation matrix is used to characterize the similarity of samples in order to establish a self-supervised sparse coding model, which aims to preserve the local structure of codings for similar samples. Finally, a numerical algorithm utilizing the alternating direction method of multipliers (ADMM) is developed to obtain the approximate solution. Experiments on several publicly available datasets validate the effectiveness and efficiency of our proposed approach compared with existing state-of-the-art methods.

Multiview clustering thrives in applications where views are collected in advance by extracting consistent and complementary information among views. However, it overlooks scenarios where data views are collected sequentially, i.e., real-time data. Due to privacy issues or memory burden, previous views are not available with time in these situations. Some methods are proposed to handle it but are trapped in a stability-plasticity dilemma. In specific, these methods undergo a catastrophic forgetting of prior knowledge when a new view is attained. Such a catastrophic forgetting problem (CFP) would cause the consistent and complementary information hard to get and affect the clustering performance. To tackle this, we propose a novel method termed contrastive continual multiview clustering with filtered structural fusion (CCMVC-FSF). Precisely, considering that data correlations play a vital role in clustering and prior knowledge ought to guide the clustering process of a new view, we develop a data buffer to store filtered structural information and utilize it to guide the generation of a robust partition matrix via contrastive learning. Additionally, to address the high complexity involved in acquiring and storing structural information, we propose a sampling strategy called clustering then sample. Furthermore, we theoretically connect CCMVC-FSF with semisupervised learning and knowledge distillation. Extensive experiments exhibit the excellence of the proposed method. Our code is publicly available at https://github.com/wanxinhang/CCMVC-FSF/.

Accurate and effective fruit tracking and counting are crucial for estimating tomato yield. In complex field environments, occlusion and overlap of tomato fruits and leaves often lead to inaccurate counting. To address these issues, this study proposed an improved lightweight YOLO11n network and an optimized region tracking-counting method, which estimates the quantity of tomatoes at different maturity stages. An improved lightweight YOLO11n network was employed for tomato detection and semantic segmentation, which was combined with the C3k2-F, Generalized Intersection over Union (GIoU), and Depthwise Separable Convolution (DSConv) modules. The improved lightweight YOLO11n model is adaptable to edge computing devices, enabling tomato yield estimation while maintaining high detection accuracy. An optimized region tracking-counting method was proposed, combining target tracking and region detection to count the detected fruits. The particle swarm optimization (PSO) algorithm was used to optimize the detection region, thus enhancing the counting accuracy. In terms of network lightweighting, compared to the original, the improved YOLO11n network significantly reduces the number of parameters and Giga Floating-point Operations Per Second (GFLOPs) by 0.22 M and 2.5 G, while achieving detection and segmentation accuracies of 91.3% and 90.5%, respectively. For fruit counting, the results showed that the proposed region tracking-counting method achieved a mean counting error (MCE) of 6.6%, representing a reduction of 5.0% and 2.1% compared to the Bytetrack and cross-line counting methods, respectively. Therefore, the proposed method provided an effective approach for non-contact, accurate, efficient, and real-time intelligent yield estimation for tomatoes.