Development of a vegetation canopy reflectance sensor and its diurnal applicability under clear sky conditions.

The spectral reflectance provides valuable information regarding vegetation growth and plays an important role in agriculture, forestry, and grassland management. In this study, a small, portable vegetation canopy reflectance (VCR) sensor that can operate throughout the day was developed. The sensor includes two optical bands at 710 nm and 870 nm, with the light separated by filters, and has a field of view of 28°.

Crop insect pest detection based on dilated multi-scale attention U-Net.

Background: Crop pests seriously affect the yield and quality of crops. Accurately and rapidly detecting and segmenting insect pests in crop leaves is a premise for effectively controlling insect pests.

Methods: Aiming at the detection problem of irregular multi-scale insect pests in the field, a dilated multi-scale attention U-Net (DMSAU-Net) model is constructed for crop insect pest detection.

MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA-Disease Association.

A growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of great significance for disease screening and treatment. In the early stages, the identification of associations between miRNAs and diseases demanded laborious and time-consuming biological experiments that often carried a substantial risk of failure.

Crop pest detection by three-scale convolutional neural network with attention.

Crop pests seriously affect the yield and quality of crop. To timely and accurately control crop pests is particularly crucial for crop security, quality of life and a stable agricultural economy. Crop pest detection in field is an essential step to control the pests.

Dilated convolution capsule network for apple leaf disease identification.

Accurate and rapid identification of apple leaf diseases is the basis for preventing and treating apple diseases. However, it is challenging to identify apple leaf diseases due to their various symptoms, different colors, irregular shapes, uneven sizes, and complex backgrounds. To reduce computational cost and improve training results, a dilated convolution capsule network (DCCapsNet) is constructed for apple leaf disease identification based on a capsule network (CapsNet) and two dilated Inception modules with different dilation rates.