Fruit yield estimation is crucial for establishing fruit harvest and marketing strategies. Recently, computer vision and deep learning techniques have been used to estimate citrus fruit yield and have exhibited notable fruit detection ability. However, computer-vision-based citrus fruit counting has two key limitations: inconsistent fruit detection accuracy and double-counting of the same fruit. Using oranges as the experimental material, this paper proposes a deep-learning-based orange counting algorithm using video sequences to help overcome these problems. The algorithm consists of two sub-algorithms, OrangeYolo for fruit detection and OrangeSort for fruit tracking. The OrangeYolo backbone network is partially based on the YOLOv3 algorithm, which has been improved upon to detect small objects (fruits) at multiple scales. The network structure was adjusted to detect small-scale targets while enabling multiscale target detection. A channel attention and spatial attention multiscale fusion module was introduced to fuse the semantic features of the deep network with the shallow textural detail features. OrangeYolo can achieve mean Average Precision (mAP) values of 0.957 in the citrus dataset, higher than the 0.905, 0.911, and 0.917 achieved with the YOLOv3, YOLOv4, and YOLOv5 algorithms. OrangeSort was designed to alleviate the double-counting problem associated with occluded fruits. A specific tracking region counting strategy and tracking algorithm based on motion displacement estimation were established. Six video sequences taken from two fields containing 22 trees were used as the validation dataset. The proposed method showed better performance (Mean Absolute Error (MAE) = 0.081, Standard Deviation (SD) = 0.08) than video-based manual counting and produced more accurate results than the existing standards Sort and DeepSort (MAE = 0.45 and 1.212; SD = 0.4741 and 1.3975).

Alzheimer's disease is a neurocognitive disorder and one of the contributing factors to dementia. According to the World Health Organization, this disease has a sig-nificant impact on the global population's health, with the number of affected individuals steadily increasing each year. Amidst rapid technological development, the use of artificial intelligence has significantly expanded into the field of medical diagnostics, encompassing areas such as the analysis of medical images, drug development, design of personalized treatment plans, and disease prediction and treatment. Deep learning, which is an important branch in the field of artificial intelligence, is playing a key role in solving several medical challenges by providing important technical support for the early detection, diagnosis, and treatment of Alzheimer's disease. Given this context, this review aims to explore the differences between conventional methods and artificial intelligence techniques in Alzheimer's disease research. Additionally, it aims to summarize current non-invasive and portable techniques for detection of Alzheimer's disease, offering support and guidance for the future prediction and management of the disease.

Fruit detection and counting are essential tasks for horticulture research. With computer vision technology development, fruit detection techniques based on deep learning have been widely used in modern orchards. However, most deep learning-based fruit detection models are generated based on fully supervised approaches, which means a model trained with one domain species may not be transferred to another. There is always a need to recreate and label the relevant training dataset, but such a procedure is time-consuming and labor-intensive. This paper proposed a domain adaptation method that can transfer an existing model trained from one domain to a new domain without extra manual labeling. The method includes three main steps: transform the source fruit image (with labeled information) into the target fruit image (without labeled information) through the CycleGAN network; Automatically label the target fruit image by a pseudo-label process; Improve the labeling accuracy by a pseudo-label self-learning approach. Use a labeled orange image dataset as the source domain, unlabeled apple and tomato image dataset as the target domain, the performance of the proposed method from the perspective of fruit detection has been evaluated. Without manual labeling for target domain image, the mean average precision reached 87.5% for apple detection and 76.9% for tomato detection, which shows that the proposed method can potentially fill the species gap in deep learning-based fruit detection.

In recent years, deep-learning-based fruit-detection technology has exhibited excellent performance in modern horticulture research. However, deploying deep learning algorithms in real-time field applications is still challenging, owing to the relatively low image processing capability of edge devices. Such limitations are becoming a new bottleneck and hindering the utilization of AI algorithms in modern horticulture. In this paper, we propose a lightweight fruit-detection algorithm, specifically designed for edge devices. The algorithm is based on Light-CSPNet as the backbone network, an improved feature-extraction module, a down-sampling method, and a feature-fusion module, and it ensures real-time detection on edge devices while maintaining the fruit-detection accuracy. The proposed algorithm was tested on three edge devices: NVIDIA Jetson Xavier NX, NVIDIA Jetson TX2, and NVIDIA Jetson NANO. The experimental results show that the average detection precision of the proposed algorithm for orange, tomato, and apple datasets are 0.93, 0.847, and 0.850, respectively. Deploying the algorithm, the detection speed of NVIDIA Jetson Xavier NX reaches 21.3, 24.8, and 22.2 FPS, while that of NVIDIA Jetson TX2 reaches 13.9, 14.1, and 14.5 FPS and that of NVIDIA Jetson NANO reaches 6.3, 5.0, and 8.5 FPS for the three datasets. Additionally, the proposed algorithm provides a component add/remove function to flexibly adjust the model structure, considering the trade-off between the detection accuracy and speed in practical usage.

With the development of signal analysis technology and artificial intelligence, surface electromyography (sEMG) signal gesture recognition is widely used in rehabilitation therapy, human-computer interaction, and other fields. Deep learning has gradually become the mainstream technology for gesture recognition. It is necessary to consider the characteristics of the surface EMG signal when constructing the deep learning model. The surface electromyography signal is an information carrier that can reflect neuromuscular activity. Under the same circumstances, a longer signal segment contains more information about muscle activity, and a shorter segment contains less information about muscle activity. Thus, signals with longer segments are suitable for recognizing gestures that mobilize complex muscle activity, and signals with shorter segments are suitable for recognizing gestures that mobilize simple muscle activity. However, current deep learning models usually extract features from single-length signal segments. This can easily cause a mismatch between the amount of information in the features and the information needed to recognize gestures, which is not conducive to improving the accuracy and stability of recognition. Therefore, in this article, we develop a long short-term transformer feature fusion network (referred to as LST-EMG-Net) that considers the differences in the timing lengths of EMG segments required for the recognition of different gestures. LST-EMG-Net imports multichannel sEMG datasets into a long short-term encoder. The encoder extracts the sEMG signals’ long short-term features. Finally, we successfully fuse the features using a feature cross-attention module and output the gesture category. We evaluated LST-EMG-Net on multiple datasets based on sparse channels and high density. It reached 81.47, 88.24, and 98.95% accuracy on Ninapro DB2E2, DB5E3 partial gesture, and CapgMyo DB-c, respectively. Following the experiment, we demonstrated that LST-EMG-Net could increase the accuracy and stability of various gesture identification and recognition tasks better than existing networks.