Click to Correction: Interactive Bidirectional Dynamic Propagation Video Object Segmentation Network.

High-quality video object segmentation is a challenging visual computing task. Interactive segmentation can improve segmentation results. This paper proposes a multi-round interactive dynamic propagation instance-level video object segmentation network based on click interaction.

Predicting leaf nitrogen content in wolfberry trees by hyperspectral transformation and machine learning for precision agriculture.

Leaf nitrogen content (LNC) is an important indicator for scientific diagnosis of the nutrition status of crops. It plays an important role in the growth, yield and quality of wolfberry. This study aimed to develop new spectral indices (NSIs) and constructed machine learning regression (MLR) models for predicting wolfberry tree LNC.

Research on the Recognition and Tracking of Group-Housed Pigs' Posture Based on Edge Computing.

The existing algorithms for identifying and tracking pigs in barns generally have a large number of parameters, relatively complex networks and a high demand for computational resources, which are not suitable for deployment in embedded-edge nodes on farms. A lightweight multi-objective identification and tracking algorithm based on improved YOLOv5s and DeepSort was developed for group-housed pigs in this study. The identification algorithm was optimized by: (i) using a dilated convolution in the YOLOv5s backbone network to reduce the number of model parameters and computational power requirements; (ii) adding a coordinate attention mechanism to improve the model precision; and (iii) pruning the BN layers to reduce the computational requirements.

A remote sensing-based strategy for mapping potentially toxic elements of soils: Temporal-spatial-spectral covariates combined with random forest.

The selection of predictor variables is a crucial issue in building a digital mapping model of potentially toxic elements (PTEs) in soil. Traditionally, the predictor variables for mapping models of soil PTEs have been chosen from sets of spatial parameters or spectral parameters derived from geographical environmental data. However, the enrichment of soil PTEs exhibits significant variations in both spatial and temporal dimensions, with the temporal dimension often being overlooked in the selection of predictor variables for digital mapping models.

Development of a Lightweight Crop Disease Image Identification Model Based on Attentional Feature Fusion.

Crop diseases are one of the important factors affecting crop yield and quality and are also an important research target in the field of agriculture. In order to quickly and accurately identify crop diseases, help farmers to control crop diseases in time, and reduce crop losses. Inspired by the application of convolutional neural networks in image identification, we propose a lightweight crop disease image identification model based on attentional feature fusion named DSGIResNet_AFF, which introduces self-built lightweight residual blocks, inverted residuals blocks, and attentional feature fusion modules on the basis of ResNet18.

An Improved Encoder-Decoder Network Based on Strip Pool Method Applied to Segmentation of Farmland Vacancy Field.

In the research of green vegetation coverage in the field of remote sensing image segmentation, crop planting area is often obtained by semantic segmentation of images taken from high altitude. This method can be used to obtain the rate of cultivated land in a region (such as a country), but it does not reflect the real situation of a particular farmland. Therefore, this paper takes low-altitude images of farmland to build a dataset.

Detection of QTLs controlling fast kernel dehydration in maize (Zea mays L.).

In order to understand the effect of grain moisture of inbred lines at the silking and physiological maturity stages on kernel dehydration rate, 59 maize inbred lines from six subgroups were selected. Grain moisture was measured and QTLs associated with kernel dehydration were mapped. A rapid dehydration evaluation and association analysis revealed eight inbred lines with faster dehydration rate, including Yuanwu 02, K36, Zhonger/O2, Lo1125, Han 49, Qi 319, Hua 160, and PH4CV.

Genetic analyses of the major and minor locus groups of bacterial wilt resistance in tobacco using a diallel cross design.

Tobacco germplasm samples with various levels of resistance to bacterial wilt were selected to construct F1 combinations of parental inbred lines and orthogonal diallel crosses using samples collected in 2009 (15 germplasms), 2010 (15 germplasms), and 2011 (16 germplasms). A total of 1/2P (P + 1) experimental materials were used for analysis. Based on the analyses of major and minor locus groups, genetic effects on the incidence rate and index of bacterial wilt in tobacco were investigated on the 15th and 25th day during the early stage.

Molecular cloning, expression analysis and subcellular localization of a Transparent Testa 12 ortholog in brown cotton (Gossypium hirsutum L.).

Transparent Testa 12 (TT12) is a kind of transmembrane transporter of proanthocyanidins (PAs), which belongs to a membrane-localized multidrug and toxin efflux (MATE) family, but the molecular basis of PAs transport is still poorly understood. Here, we cloned a full-length TT12 cDNA from the fiber of brown cotton (Gossypium hirsutum), named GhTT12 (GenBank accession No. KF240564), which comprised 1733 bp with an open reading frame (ORF) of 1503 bp and encoded a putative protein containing 500 amino acid residues with a typical MATE conserved domain.