The sampling-based Kalman filters have been widely used for GNSS/INS of land vehicle navigation. Although many nonlinear filters are proposed by employing different approximate algorithms, most of the filters update sigma points utilize the moments match and Bayesian filter update framework. In this paper, cubature Kalman filters (CKFs) based on a class of novel sigma-point update framework (NSUF) are developed and compared in terms of accuracy and convergence rate. In order to enhance the reliability of GNSS/INS, an improved CKF, named as ArtCKF is proposed by merging the moments generated by different NSUFs, which not only addresses the problem of observation missing but also improves the efficiency of Bayesian filter update. Numerical simulation based on field test data is employed to verify the superiority of ArtCKF in land vehicle navigation. Our results indicate that the ArtCKF improves the heading of land vehicle by 71.3% compared with CKF, and makes a tradeoff between direct observable and non-direct observable state estimation under GNSS-challenged environment.

Accurately classifying the quality of apples is crucial for maximizing their commercial value. Deep learning techniques are being widely adopted for apple quality classification tasks, achieving impressive results. While existing research excels at classifying apple variety, size, shape, and defects, color and deformity analysis remain an under-explored area. Therefore, this study investigates the feasibility of utilizing convolutional neural networks (CNN) to classify the color and deformity of apples based on machine vision technology. Firstly, a custom-assembled machine vision system was constructed for collecting apple images. Then, image processing was performed to extract the largest fruit diameter from the 45 images taken for each apple, establishing an image dataset. Three classic CNN models (AlexNet, GoogLeNet, and VGG16) were employed with parameter optimization for a three-category classification task (non-deformed slice–red apple, non-deformed stripe–red apple, and deformed apple) based on apple features. VGG16 achieved the best results with an accuracy of 92.29%. AlexNet and GoogLeNet achieved 91.66% and 88.96% accuracy, respectively. Ablation experiments were performed on the VGG16 model, which found that each convolutional block contributed to the classification task. Finally, prediction using VGG16 was conducted with 150 apples and the prediction accuracy was 90.50%, which was comparable to or better than other existing models. This study provides insights into apple classification based on color and deformity using deep learning methods.

Accurate categorization and timely control of leaf diseases are crucial for citrus growth. We proposed the Multi-Models Fusion Network (MMFN) for citrus leaf diseases detection based on model fusion and transfer learning. Compared to traditional methods, the algorithm (integrating transfer learning Alexnet, VGG, and Resnet) we proposed can address the issues of limited categories, slow processing speed, and low recognition accuracy. By constructing efficient deep learning models and training and optimizing them with a large dataset of citrus leaf images, we ensured the broad applicability and accuracy of citrus leaf disease detection, achieving high-precision classification. Herein, various deep learning algorithms, including original Alexnet, VGG, Resnet, and transfer learning versions Resnet34 (Pre_Resnet34) and Resnet50 (Pre_Resnet50) were also discussed and compared. The results demonstrated that the MMFN model achieved an average accuracy of 99.72% in distinguishing between diseased and healthy leaves. Additionally, the model attained an average accuracy of 98.68% in the classification of multiple diseases (citrus huanglongbing (HLB), greasy spot disease and citrus canker), insect pests (citrus leaf miner), and deficiency disease (zinc deficiency). These findings conclusively illustrate that deep learning model fusion networks combining transfer learning and integration algorithms can automatically extract image features, enhance the automation and accuracy of disease recognition, demonstrate the significant potential and application value in citrus leaf disease classification, and potentially drive the development of smart agriculture.

Nondestructive diagnostics of soluble sugar, total nitrogen and their ratio of tomato leaves in greenhouse by polarized spectra–hyperspectral data fusion

Lǎo huǒ liàng tāng (Cantonese slow-cooked soup, CSCS) is popular in Guangdong, China, and is consumed by Cantonese people worldwide as a delicious appetizer. Because CSCS serves as an important part of family healthcare, medicinal plants and plant-derived products are major components of CSCS. However, a collated record of the diverse plant species and an ethnobotanical investigation of CSCS is lacking. Because of globalization along with a renewed interest in botanical and food therapy, CSCS has attracted a growing attention in soup by industries, scientists, and consumers. This study represents the first attempt to document the plant species used for CSCS in Guangdong, China, and the associated ethnomedical function of plants, including their local names, part(s) used, flavors, nature, preparation before cooking, habitats, and conservation status. In 2014–2017, participatory approaches, open-ended conversations, and semi-structured interviews were conducted with 63 local people and 48 soup restaurant owners (111 interviews) to better understand the biocultural context of CSCS, emphasizing ethnobotanical uses of plants in Guangdong Province, China. Product samples and voucher specimens were collected for taxonomic identification. Mention Index (QI), frequency of use index (FUI), and economic index (EI) were adopted to evaluate the significance of each plant in the food supply. A total of 97 plant species belonging to 46 families and 90 genera were recorded as having been used in CSCS in the study area. Recorded menus consisted of one or several plant species, with each one used for different purposes. They were classified into 11 functions, with clearing heat being the most common medicinal function. Of the 97 species, 19 grew only in the wild, 8 species were both wild and cultivated, and 70 species were cultivated. Roots and fruits were the most commonly used plant parts in the preparation of CSCS. According to the national evaluation criteria, six of these species are listed on “China’s red list” including two endangered, two critically endangered, one near-threatened, and one vulnerable species. The QI, FUI, and EI of the 97 species in the study varied between 0.09 and 1, 0.23 and 9.95, and 0.45 and 6.58, respectively. As an important part of Cantonese culture, CSCS has been popularized as a local cuisine with a healthcare function. CSCS also reflects the plant species richness and cultural diversity of Guangdong Province. Future research on the safety and efficacy of CSCS as well as on ecological and cultural conservation efforts is needed for the sustainable growth of China’s botanical and medicinal plant industry.

Mostly, the conditions within an ecosystem as well as weather of a field affect crop productivity greatly. Different weather conditions produce different effects and different impact on the quality of the crop field or the ecosystem. Weather elements form a chain reaction, as the atmosphere is not the only one being affected. Atmospheric air temperature, vapor pressure and relative humidity or moisture content can act together and form diverse effects on crops. These diverse effects turn to reduce radiation which is necessary for plants, or increase rainfall patterns. Consistent high temperatures can increase the heat transfer to local bodies of water in addition to heating the air. Monitoring the climate and the weather conditions are important not only as an environmental baseline, but to maintain quality working conditions, marine studies and recreational safety. The parameters of climate are measurable, for example, atmospheric vapor pressure, temperature, precipitation and solar radiation, can be captured and recorded daily on the Smart Arduino Sensor Integrated Drone. Means and extreme datasets, maximum and minimum weather trends with deviations of lengthy time series would be calculated for each of these climate parameters which were considered in this study. These results are a simple form of climate indices, as they already describe changes in climate. All the readings and datasets are recorded on a cloud platform, as well as, in an installed microchip on the drone. Data synchronization is done with MAT-LAB and Arduino Programming Rule.