Plant architecture and leaf color are important factors influencing cotton fiber yield. In this study, based on genetic analysis, stem paraffin sectioning, and phytohormone treatments, we showed that the dwarf-red (DR) cotton mutant is a gibberellin-sensitive mutant caused by a mutation in a single dominant locus, designated GhDR. Using bulked segregant analysis (BSA) and genotyping by target sequencing (GBTS) approaches, we located the causative mutation to a ~197-kb genetic interval on chromosome A09 containing 25 annotated genes. Based on gene annotation and expression changes between the mutant and normal plants, GH_A09G2280 was considered to be the best candidate gene responsible for the dwarf and red mutant phenotypes. A 2-nucleotide deletion was found in the coding region of GhDR/GH_A09G2280 in the DR mutant, which caused a frameshift and truncation of GhDR. GhDR is a homolog of Arabidopsis AtBBX24, and encodes a B-box zinc finger protein. The frameshift deletion eliminated the C-terminal nuclear localization domain and the VP domain of GhDR, and altered its subcellular localization. A comparative transcriptome analysis demonstrated downregulation of the key genes involved in gibberellin biosynthesis and the signaling transduction network, as well as upregulation of the genes related to gibberellin degradation and the anthocyanin biosynthetic pathway in the DR mutant. The results of this study revealed the potential molecular basis by which plant architecture and anthocyanin accumulation are regulated in cotton.  

Large-scale crop mapping using remote sensing data is of great significance for agricultural production, food security and the sustainable development of human societies. Winter rapeseed is an important oil crop in China that is mainly distributed in the Yangtze River Valley. Traditional winter rapeseed mapping practices are insufficient since they only use the spectral characteristics during the critical phenological period of winter rapeseed, which are usually limited to a small region and cannot meet the needs of large-scale applications. In this study, a novel phenology-based winter rapeseed index (PWRI) was proposed to map winter rapeseed in the Yangtze River Valley. PWRI expands the date window for distinguishing winter rapeseed and winter wheat, and it has good separability throughout the flowering period of winter rapeseed. PWRI also improves the separability of winter rapeseed and winter wheat, which traditionally have been two easily confused winter crops. A PWRI-based method was applied to the Middle Reaches of the Yangtze River Valley to map winter rapeseed on the Google Earth Engine platform. Time series composited Sentinel-2 data were used to map winter rapeseed with 10 m resolution. The mapping achieved a good result with overall accuracy and kappa coefficients exceeding 92% and 0.85, respectively. The PWRI-based method provides a new solution for high spatial resolution winter rapeseed mapping at a large scale.

Accurate and timely classification of diseases during strawberry planting can help growers deal with them in timely manner, thereby reducing losses.  However, the classification of strawberry diseases in real planting environments is facing severe challenges, including complex planting environments, multiple disease categories with small differences, and so on.  Although recent mobile vision technology based deep learning has achieved some success in overcoming the above problems, a key problem is how to construct a non-destructive, fast and convenient method to improve the efficiency of strawberry disease identification for the multi-region, multi-space and multi-time classification requirements.  We develop and evaluate a rapid, low-cost system for classifying diseases in strawberry cultivation.  This involves designing an easy-to-use cloud-based strawberry disease identification system, combined with our novel self-supervised multi-network fusion classification model, which consists of a Location network, a Feedback network and a Classification network to identify the categories of common strawberry diseases.  With the help of a novel self-supervision mechanism, the model can effectively identify diseased regions of strawberry disease images without the need for annotations such as bounding boxes.  Using accuracy, precision, recall and F₁ to evaluate the classification effect, the results of the test set are 92.48, 90.68, 86.32 and 88.45%, respectively.  Compared with popular Convolutional Neural Networks (CNN) and five other methods, our network achieves better disease classification effect.  Currently, the client (mini program) has been released on the WeChat platform.  The mini program has perfect classification effect in the actual test, which verifies the feasibility and effectiveness of the system, and can provide a reference for the intelligent research and application of strawberry disease identification.

To separate the proteins related to pigment synthesis in green colored fiber (GCF), we performed a comparative proteomic analysis to identify the differentially expressed proteins between green cotton fiber and a white near-isogenic line (NIL).  One differential spot identified as phenylocumaran benzylic ether redutase-like protein (PCBER) was expressed only in GCF, but was not found in white colored fiber (WCF) at any time points.  Since PCBER was a key enzyme in lignans biosynthesis, total lignans were extracted from GCF and WCF and their content was determined by using a chromotropic acid spectrophotometric method.  The results showed that total lignans content in GCF was significantly higher than that in WCF.  The qPCR analysis for two PLR genes associated with lignans biosynthesis showed that the expression level of two genes was much higher in GCF than that in WCF at 24 and 27 days post anthesis (DPA), which may be responsible for the higher lignans content in GCF.  Our study suggested that PCBER and lignans may be responsible for the color difference between GCF and WCF.  Additionally, p-dimethylaminocinnamaldehyde (DMACA) staining demonstrated that the pigment in GCF was not proanthocyanidins, and was different from that in brown colored fiber (BCF).  This study provided new clues for uncovering the molecular mechanisms related to pigment biosynthesis in GCF.