Temporal changes in Lycium barbarum fruit separation force and hardness during selective harvesting.

Lycium barbarum, a plant belonging to the Solanaceae family, is widely used in China due to its abundant nutritional value. Although the current mechanized harvesting method of L. barbarum has effectively minimized production expenses, it continues to have the challenge of inconsistent quality of the produced L.

LiDAR and Deep Learning-Based Standing Tree Detection for Firebreaks Applications.

Forest fire prevention is very important for the protection of the ecological environment, which requires effective prevention and timely suppression. The opening of the firebreaks barrier contributes significantly to forest fire prevention. The development of an artificial intelligence algorithm makes it possible for an intelligent belt opener to create the opening of the firebreak barrier.

Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning.

To date, most existing forest fire smoke detection methods rely on coarse-grained identification, which only distinguishes between smoke and non-smoke. Thus, non-fire smoke and fire smoke are treated the same in these methods, resulting in false alarms within the smoke classes. The fine-grained identification of smoke which can identify differences between non-fire and fire smoke is of great significance for accurate forest fire monitoring; however, it requires a large database.

Determining the Stir-Frying Degree of Praeparatus Based on Deep Learning and Transfer Learning.

Gardeniae Fructus (GF) is one of the most widely used traditional Chinese medicines (TCMs). Its processed product, Praeparatus (GFP), is often used as medicine; hence, there is an urgent need to determine the stir-frying degree of GFP. In this paper, we propose a deep learning method based on transfer learning to determine the stir-frying degree of GFP.

Full-Scale Fire Smoke Root Detection Based on Connected Particles.

Smoke is an early visual phenomenon of forest fires, and the timely detection of smoke is of great significance for early warning systems. However, most existing smoke detection algorithms have varying levels of accuracy over different distances. This paper proposes a new smoke root detection algorithm that integrates the static and dynamic features of smoke and detects the final smoke root based on clustering and the circumcircle.

An Embedded Portable Lightweight Platform for Real-Time Early Smoke Detection.

The advances in developing more accurate and fast smoke detection algorithms increase the need for computation in smoke detection, which demands the involvement of personal computers or workstations. Better detection results require a more complex network structure of the smoke detection algorithms and higher hardware configuration, which disqualify them as lightweight portable smoke detection for high detection efficiency. To solve this challenge, this paper designs a lightweight portable remote smoke front-end perception platform based on the Raspberry Pi under Linux operating system.

Homogenized modeling methodology for 18650 lithium-ion battery module under large deformation.

Effective lithium-ion battery module modeling has become a bottleneck for full-size electric vehicle crash safety numerical simulation. Modeling every single cell in detail would be costly. However, computational accuracy could be lost if the module is modeled by using a simple bulk material or rigid body.