Ripening Study Based on Multi-Structural Inversion of Cherry Tomato qMRI.

This study introduces a non-destructive, quantitative method using low-field MRI to assess moisture mobility and content distribution in cherry tomatoes. This study developed an advanced 3D non-local mean denoising model to enhance tissue feature analysis and applied an optimized TransUNet model for structural segmentation, obtaining multi-echo data from six tissue types. The structural T2 relaxation inversion was refined by integrating an ACS-CIPSO algorithm.

Quantitative MRI post-processing algorithm and visualization research based on moisture status detection of winter jujube.

Quantitative Magnetic Resonance Imaging (qMRI) offers precise measurements of the relaxation characteristics of microstructures, representing a cutting-edge method in non-destructive fruit analysis. This study aims to visualize information on changes in moisture status and distribution at the subcellular level of winter jujube. The 0.

Droplet Deposition Distribution Prediction Method for a Six-Rotor Plant Protection UAV Based on Inverse Distance Weighting.

The aim of this work is to establish a method for real-time calculating droplet deposition distribution of a six-rotor plant protection unmanned aerial vehicle (UAV). The numerical simulation of the airflow field was carried out using computational fluid dynamics (CFD). The airflow field distribution was obtained under seven flight speeds, six flight heights, and seven crosswind speeds.

Precision Seeding Compensation and Positioning Based on Multisensors.

The current multi-row planter always leads to uneven seeding spacing between rows while seeding in curve paths, which causes uneven growth, a cost increase of production and management, and reduced yield. With the development of smart farming technology, a curve seeding compensation and precise positioning model is proposed in the paper to calculate the real-time speed and position of each seeding unit based on the information from multisensors, such as GNSS and IMU, and to predict the next seeding position to achieve uniform seeding on the curve and improve the unit yield of crops. MATLAB Simulink simulation experiments show that the seeding pass rate of the model is 99.

Recent advances in methods for root phenotyping.

Roots assist plants in absorbing water and nutrients from soil. Thus, they are vital to the survival of nearly all land plants, considering that plants cannot move to seek optimal environmental conditions. Crop species with optimal root system are essential for future food security and key to improving agricultural productivity and sustainability.

A Plant Disease Recognition Method Based on Fusion of Images and Graph Structure Text.

The disease image recognition models based on deep learning have achieved relative success under limited and restricted conditions, but such models are generally subjected to the shortcoming of weak robustness. The model accuracy would decrease obviously when recognizing disease images with complex backgrounds under field conditions. Moreover, most of the models based on deep learning only involve characterization learning on visual information in the image form, while the expression of other modal information rather than the image form is often ignored.