Explainable artificial intelligence has been extensively studied recently. However, the research of interpretable methods in the agricultural field has not been systematically studied. We studied the interpretability of deep learning models in different agricultural classification tasks based on the fruit leaves dataset. The purpose is to explore whether the classification model is more inclined to extract the appearance characteristics of leaves or the texture characteristics of leaf lesions during the feature extraction process. The dataset was arranged into three experiments with different categories. In each experiment, the VGG, GoogLeNet, and ResNet models were used and the ResNet-attention model was applied with three interpretable methods. The results show that the ResNet model has the highest accuracy rate in the three experiments, which are 99.11%, 99.4%, and 99.89%, respectively. It is also found that the attention module could improve the feature extraction of the model, and clarify the focus of the model in different experiments when extracting features. These results will help agricultural practitioners better apply deep learning models to solve more practical problems.

This article studies the non-fragile $H_\infty$ fault-tolerant load frequency control problem for multi-area power systems with actuator failures. At first, considering the distribution characteristics of partial failures, a stochastic distribution model is established for actuator partial failures. Next, for the perturbations of controller gains, not only the additive uncertainties but also the multiplicative uncertainties are incorporated into the fault-tolerant controller design. The coupling items which hardly directly solved have turned into a W-problem in fault-tolerant load frequency control. Furthermore, a novel failure-distribution-dependent non-fragile $H_\infty$ fault-tolerant load frequency control algorithm is given by using linear matrix inequalities technology and Lyapunov-Krasovskii functional method. Finally, a numerical example with a three-area power system is given to show the effectiveness of the proposed methods.

As it is high in value, extra virgin olive oil (EVOO) is frequently blended with inferior vegetable oils. This study presents an optical method for determining the adulteration level of EVOO with soybean oil as well as peanut oil using LED-induced fluorescence spectroscopy. Eight LEDs with central wavelengths from ultra-violet (UV) to blue are tested to induce the fluorescence spectra of EVOO, peanut oil, and soybean oil, and the UV LED of 372 nm is selected for further detection. Samples are prepared by mixing olive oil with different volume fractions of peanut or soybean oil, and their fluorescence spectra are collected. Different pre-processing and regression methods are utilized to build the prediction model, and good linearity is obtained between the predicted and actual adulteration concentration. This result, accompanied by the non-destruction and no pre-treatment characteristics, proves that it is feasible to use LED-induced fluorescence spectroscopy as a way to investigate the EVOO adulteration level, and paves the way for building a hand-hold device that can be applied to real market conditions in the future.

In this paper, an ecological driving (eco-driving) algorithm considering queue effects is proposed for connected and automated vehicles (CAVs) at unsaturated intersections in order to reduce fuel consumption and travel time. Firstly, after the traffic flow parameters are obtained using vehicle-to-infrastructure communication technology, the kinematic shockwave model is used to predict the vehicle queue length at the saturated intersections. Secondly, to decrease fuel consumption, a fuel-saving optimization problem is formulated using the estimated queue length, and for real-time control, the formulated optimization problem is decomposed into two subproblems depending on whether the CAV will stop to queue. Then, to reduce fuel consumption and travel time simultaneously, the eco-driving algorithm is designed, where the trajectory re-optimization process is implemented in order not to block the upstream vehicles. Finally, extensive simulations are carried out on VISSIM to demonstrate the control performance of the proposed eco-driving algorithm on a single CAV and on the entire traffic flow. Simulation results show that the proposed eco-driving algorithm can significantly decrease the fuel consumption and travel time of both CAVs and the traffic flow, and higher market penetration rate of CAVs can result in better control performance.

The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life.