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Swift, non-destructive detection approaches should address the problem of insufficient sensitivity when attempting to obtain and perceive live crab information in low-light environments caused by the crab's phototaxis. We propose a learning-based low-illumination image enhancer (LigED) for effective enhanced lighting and elimination of darkness in images. The camera response function was combined with the reflectance ground-truth mechanism of image decomposition. Self-attention units were then introduced in the reflectance restoration network to adjust the illumination to avoid visual defects, thus jointly strengthening the adaptability of dark-light enhancement and ability to perceive crab information. Convolutional neural network (CNN)-based detection methods can further enhance the algorithm's robustness to light and adaptability to different environments, which motivated the development of a scalable lightweight live crab detector (EfficientNet-Det0) utilizing the two-stage compound scaling CNN approach. The lightness order error and natural image quality evaluator based on the proposed methods were 251.26 and 11.60, respectively. The quality of average precision detection increased by 13.84–95.40%. The fastest detection speed of a single image was 91.74/28.41 f·s−1 using a common GPU/CPU, requiring only 15.1 MB of storage, which advocates for the utilization of LigED and EfficientNet-Det0 for the efficient detection of underwater live crabs. [ABSTRACT FROM AUTHOR]

In this work, potassium-containing Cu2SnS3 thin films (KxCu2−xSnS3) were fabricated by a simple and low-cost ultrasonic spray pyrolysis method on glass substrates with different potassium doping concentration. Effects of various potassium concentrations on the structural, morphological and optical properties of the films were systematically investigated. The results indicate that potassium doping can well enhance the growth of particle size and decrease the band gap of the film from to 1.55 to 1.26 eV. Solar cell devices with a structure of glass/FTO/K2−xCu2SnS3/CdS/ZnO/AZO have been prepared and obvious solar cell device performance improvement has been observed by 2 at% potassium doping. This potassium-containing Cu2SnS3 thin films could be a promising absorber material for high efficiency solar cell. [ABSTRACT FROM AUTHOR]

In order to improve the image denoising ability, the wavelet transform (WT) and independent component analysis (ICA) are both introduced into image denoising in this paper. Although these two algorithms have their own advantages in image denoising, they are unable to reduce noises completely, which makes it difficult to achieve ideal effect. Therefore, a new image denoising method is proposed based on the combination of WT with ICA (WT-ICA). For verifying the WT-ICA denoising method, we adopt four image denoising methods for comparison: median filtering (MF), wavelet soft thresholding (WST), ICA, and WT-ICA. From the experimental results, it is shown that WT-ICA can significantly reduce noises and get lower-noise image. Moreover, the average of WT-ICA denoising image’s peak signal to noise ratio (PSNR) is improved by 20.54% compared with noisy image and 11.68% compared with the classical WST denoising image, which demonstrates its advantage. From the performance of texture and edge detection, denoising image by WT-ICA is closer to the original image. Therefore, the new method has its unique advantage in image denoising, which lays a solid foundation for the realization of further image processing task. [ABSTRACT FROM AUTHOR]

For yield measurement of an apple orchard or the mechanical harvesting of apples, there needs to be accurate identification of the target apple fruit. However, in a natural scene, affected by the apple's growth posture and camera position, there are many kinds of apple images, such as overlapped apples; mutual shadows or leaves; stems; etc. It is a challenge to accurately locate overlapped apples. They will influence the positioning time and recognition efficiency and then affect the harvesting efficiency of apple-harvesting robots or the accuracy of orchard yield measurement. In response to this problem, an overlapped circle positioning method based on local maxima is proposed. First, the apple image is transformed into the Lab color space and segmented by the K-means algorithm. Second, some morphological processes, like erosion and dilation, are implemented to abstract the outline of the apples. Then image points are divided into central points; edge points; or outer points. Third, a fast algorithm is used to calculate every internal point's minimum distance from the edge. Then, the centers of the apples are obtained by finding the maxima among these distances. Last, the radii are acquired by figuring out the minimum distance between the center and the edge. Thus, positioning is achieved. Experimental results showed that this method can locate overlapped apples accurately and quickly when the apple contour was complete; and this has certain practicability. [ABSTRACT FROM AUTHOR]

Chinese river crabs are important aquatic products in China, and the accurate operation of aquatic plants cleaning workboat is an urgent need for solving various problems in the aquaculture process. In order to achieve the accurate navigation positioning, this article introduces the visual-aided navigation system and combines the advantages of particle filter in nonlinear and non-Gaussian systems. Meanwhile, the generalized regression neural network is used to adjust the particle weights so that the samples are closer to the posterior density, thus avoiding the phenomenon of particle degradation and keeping the diversity of particles. In order to improve the network performance, the fruit fly optimization algorithm is introduced to adjust the smoothing factor of transfer function for the generalized regression neural network model layer. On this basis, the location filtering navigation method based on fruit fly optimization algorithm-generalized regression neural network-particle filter is proposed. According to the simulation results, the meanR of root-mean-square error of the proposed fruit fly optimization algorithm-generalized regression neural network- particle filter method decreases by 12.39% and 6.87%, respectively, compared with those of particle filter and generalized regression neural network methods, and the meanT of running time decreases by 16.04% and 9.14%, respectively. From the repeated experiments on the aquatic plants cleaning workboat in crab ponds, the latitude error of the proposed method decreases by 23.45% and 12.68%, respectively, and that the longitude error decreases by 29.11% and 17.65%, respectively, compared with those of particle filter and generalized regression neural network methods. It is proved that our proposed method can effectively improve the navigation positioning accuracy of aquatic plants cleaning workboat. [ABSTRACT FROM AUTHOR]

Highlights • Aggressive pigs were separated from the herd and extracted continuously. • Head and 4 kink points of pigs were located and used as feature points. • Kinetic energy model was built by analysing motion of these 5 feature points. • Kinetic energy difference between adjacent frames was used to recognise aggression. • The proposed algorithm has higher accuracy, sensitivity, specificity and precision. Abstract The aim of this study is to build a kinetic energy model to recognise aggressive behaviours in pigs. Pig mixing experiments were performed 10 times. In each experiment, 7 pigs from 3 pens were mixed and then videos of the first 3 h after mixing and the 3 h after 24 h were selected as experimental data. A 30 h data of 5 groups of pigs were used as training set and a 30 h data of another 5 groups of pigs as test set. Firstly, according to connected area and adhesion index, aggressive pigs were separated from the herd and then key frame sequences with aggression were extracted. Secondly, head and 4 kink points of pigs were selected as feature points. By analysing motion of these points, kinetic energy of 2 aggressive pigs was calculated and kinetic energy difference between adjacent frames was selected as features. Finally, these features were trained by using hierarchical clustering to obtain the thresholds of high and medium aggression. These thresholds were used to design the rule of aggression recognition. By using the proposed algorithm, high aggression could be recognised with an accuracy of 95.8%, a sensitivity of 92.8%, specificity of 96.7% and precision of 90.2%, and medium aggression could be recognised with an accuracy of 92.3%, a sensitivity of 92.6%, specificity of 91.9% and precision of 93.0%. The results indicate that this algorithm can be used to recognise aggressive behaviours of pigs. [ABSTRACT FROM AUTHOR]

Because of the unstructured characteristics of natural orchards, the efficient detection and segmentation applications of green fruits remain an essential challenge for intelligent agriculture. Therefore, an innovative fruit segmentation method based on deep learning, termed SE-COTR (segmentation based on coordinate transformer), is proposed to achieve accurate and real-time segmentation of green apples. The lightweight network MobileNetV2 is used as the backbone, combined with the constructed coordinate attention-based coordinate transformer module to enhance the focus on effective features. In addition, joint pyramid upsampling module is optimized for integrating multiscale features, making the model suitable for the detection and segmentation of target fruits with different sizes. Finally, in combination with the outputs of the function heads, the dynamic convolution operation is applied to predict the instance mask. In complex orchard environment with variable conditions, SE-COTR achieves a mean average precision of 61.6% with low complexity for green apple fruit segmentation at severe occlusion and different fruit scales. Especially, the segmentation accuracy for small target fruits reaches 43.3%, which is obviously better than other advanced segmentation models and realizes good recognition results. The proposed method effectively solves the problem of low accuracy and overly complex fruit segmentation models with the same color as the background and can be built in portable mobile devices to undertake accurate and efficient agricultural works in complex orchard., (Copyright © 2022 Zhifen Wang et al.)