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This study aims to investigate time-harmonic wave propagation in a chiral porous thermoelastic solid under strain gradient theory (SGT), focusing on identifying and characterizing distinct wave modes within the medium.

Using Iesan's gradient theory, which incorporates chiral effects and accommodates second sound phenomena, the authors derive mathematical formulations for the velocities and attenuations of eight propagating waves: four dilatational waves and two pairs of coupled shear waves (one left circularly polarized, the other right). Numerical simulations are performed for a specific model, exploring the influence of various parameters on wave propagation.

The authors establish that the medium supports four dilatational waves, including a microstretch-associated wave, and four shear waves, distinguished by their chiral-induced characteristics. The results highlight the frequency-dependent dispersive nature of all propagating waves and establish connections with existing theoretical frameworks, demonstrating the broader applicability of our findings.

The characteristics of wave propagation in chiral media examined here can enhance our understanding of chiral medium behavior. This knowledge is crucial for developing materials with pronounced chiral effects, surpassing those found in natural chiral materials like bone, quartz, sugar and wood. Advances in artificial chiral materials are driven by their superior toughness, durability and other beneficial properties. Consequently, this study has potential applications across various fields, including the design of chiral broadband absorbers and filters, the production of artificial bones and medical devices, aeronautical engineering and beyond.

This research extends existing theories and deepens the understanding by exploring wave behaviors in chiral media, advancing this emerging field.

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.