Thidiazuron (TDZ) is widely used as a defoliant to induce leaf abscission in cotton. However, the underlying molecular mechanism is still unclear. In this study, RNA-seq and enzyme-linked immunosorbent assays (ELISA) were performed to reveal the dynamic transcriptome profiling and the change of endogenous phytohormones upon TDZ treatment in leaf, petiole, and abscission zone (AZ). We found that TDZ induced the gene expression of ethylene biosynthesis and signal, and promoted ethylene accumulation earlier in leaf than that in AZ. While TDZ down-regulated indole-3-acetic acid (IAA) biosynthesis genes mainly in leaf and IAA signal and transport genes. Furthermore, the IAA content reduced more sharply in the leaf than that in AZ to change the auxin gradient for abscission. TDZ suppressed CTK biosynthesis genes and induced CTK metabolic genes to reduce the IPA accumulation for the reduction of ethylene sensitivity. Furthermore, TDZ regulated the gene expression of abscisic acid (ABA) biosynthesis and signal and induced ABA accumulation between 12–48 h, which could up-regulate ABA response factor genes and inhibit IAA transporter genes. Our data suggest that TDZ orchestrates metabolism and signal of ethylene, auxin, and cytokinin, and also the transport of auxin in leaf, petiole, and AZ, to control leaf abscission.

In order to efficiently determine genotypic differences in rooting patterns of crops, novel hardware and software are needed simultaneously to characterize dynamics of root development. We describe a prototype robotic monitoring platform—the RhizoChamber-Monitor for analyzing growth patterns of plant roots automatically. The RhizoChamber-Monitor comprises an automatic imaging system for acquiring sequential images of roots which grow on a cloth substrate in custom rhizoboxes, an automatic irrigation system and a flexible shading arrangement. A customized image processing software was developed to analyze the spatio-temporal dynamics of root growth from time-course images of multiple plants. This software can quantify overall growth of roots and extract detailed growth traits (e.g. dynamics of length and diameter) of primary roots and of individual lateral roots automatically. It can also identify local growth traits of lateral roots (pseudo-mean-length and pseudo-maximum-length) semi-automatically. Two cotton genotypes were used to test both the physical platform and the analysis software. The combination of hardware and software is expected to facilitate quantification of root geometry and its spatio-temporal growth patterns, and therefore to provide opportunities for high-throughput root phenotyping in support of crop breeding to optimize root architecture.

Cotton constitutes 81% of the world’s natural fibers. Accurate and rapid cotton yield estimation is important for cotton trade and agricultural policy development. Therefore, we developed a remote sensing index that can intuitively represent cotton boll characteristics and support cotton yield estimation by extracting cotton boll pixels. In our study, the Density of open Cotton boll Pixels (DCPs) was extracted by designing different cotton boll indices combined with the threshold segmentation method. The relationship between DCP and field survey datasets, the Density of Total Cotton bolls (DTC), and yield were compared and analyzed. Five common yield estimation models, Linear Regression (LR), Support Vector Regression (SVR), Classification and Regression Trees (CART), Random Forest (RF), and K-Nearest Neighbors (KNN), were implemented and evaluated. The results showed that DCP had a strong correlation with yield, with a Pearson correlation coefficient of 0.84. The RF method exhibited the best yield estimation performance, with average R² and rRMSE values of 0.77 and 7.5%, respectively (five-fold cross-validation). This study showed that RedGreenBlue (RGB) and Near Infrared Red (NIR) normalized, a normalized form index consisting of the RGB and NIR bands, performed best.

Manual removal of the main stem tips of cotton (Gossypium hirsutum L.) is a traditional topping approach in China. However, chemical topping has become an inevitable trend. Therefore, it is of great significance to study the effect and appropriate concentration of agents for advancement of cotton whole process mechanization management technology. A two-year field experiment was conducted to evaluate the effects of different methods of topping on cotton yield and plant architecture in Shihezi, Xinjiang, China. Plant topping included manual topping, non-topping as the control, and chemical topping (high, medium, and low concentrations of topping agent) to determine a suitable topping method and topping agent concentration for machine-picked cotton. Chemical topping was performed using fortified mepiquat chloride (DPC⁺). Chemical topping and manual topping increased seed cotton yield compared with non-topping. Compared with non-topping, high, medium, and low concentrations of topping agent significantly increased the yield by 19.5–27.9%, 24.1–29.4%, and 24.3–28.4%, respectively. Topping treatment mainly regulated yield and total boll number per unit area by affecting the boll number per plant and had a certain positive effect on fiber strength but no significant effect on boll weight. Chemical topping affected both vertical and horizontal plant architecture characteristics of cotton; the plant height of low-, medium-, and high-concentration treatments increased by 7.2–11.4 cm, 4.0–5.7 cm, and 2.3–4.4 cm, respectively, compared with manual topping and decreased by 5.1–7.8 cm, 8.3–13.5 cm, and 9.4–16.9 cm, respectively, compared with non-topping. The number of main stem internodes was significantly different between high- and low-concentration treatments, which showed that the higher the concentration, the lower the number of the main stem internodes. Chemical topping controlled the increase in the length of the upper branches, the higher the concentration, the lower the increase in the length of the upper fruit branch. Compared with non-topping, the medium concentration of topping agent controlled the number of fruit branches, similar to manual topping. The role of upper internode length of cotton cannot be ignored under chemical topping. The peak leaf area index (LAI) of medium- and high-concentration treatments was delayed for 1 week in the late growth period (after topping for 28 d) compared with manual topping. The LAI values were high, and the duration of high values was prolonged. The optimal chemical topping agent was the medium concentration.

Thidiazuron (TDZ) is a widely used chemical defoliant in cotton and can stimulate the production of ethylene in leaves, which is believed to be the key factor in inducing leaf abscission. Ethephon (Eth) can also stimulate ethylene production in leaves, but it is less effective in promoting leaf shedding. In this study, the enzyme-linked immunosorbent assays (ELISA) and RNA-seq were used to determine specific changes at hormonal levels as well as transcriptomic mechanisms induced by TDZ compared with Eth. The TDZ significantly reduced the levels of auxin and cytokinin in cotton leaves, but no considerable changes were observed for Eth. In addition, TDZ specifically increased the levels of brassinosteroids and jasmonic acid in the leaves. A total of 13 764 differentially expressed genes that specifically responded to TDZ were identified by RNA-seq. The analysis of KEGG functional categories suggested that the synthesis, metabolism, and signal transduction of auxin, cytokinin, and brassinosteroid were all involved in the TDZ-induced abscission of cotton leaves. Eight auxin transport genes (GhPIN1-c_D, GhPIN3_D, GhPIN8_A, GhABCB19-b_A, GhABCB19-b_D, GhABCB2-b_D, GhLAX6_A, and GhLAX7_D) specifically responded to TDZ. The pro35S::GhPIN3a::YFP transgenic plants showed lower defoliation than the wild type treated with TDZ, and YFP fluorescence in leaves was almost extinguished after treatment with TDZ rather than Eth. This provides direct evidence that GhPIN3a is involved in the leaf abscission induced by TDZ. We found that 959 transcription factors (TFs) specifically responded to TDZ, and a co-expression network analysis (WGCNA) showed five hub TFs (GhNAC72, GhWRKY51, GhWRKY70, GhWRKY50, and GhHSF24) during chemical defoliation with TDZ. Our work sheds light on the molecular basis of TDZ-induced leaf abscission in cotton.

Defoliation and accelerating ripening are important measures for cotton mechanization, and judging the time of defoliation and accelerating the ripening and harvest of cotton relies heavily on the boll opening rate, making it a crucial factor to consider. The traditional methods of cotton opening rate determination are time-consuming, labor-intensive, destructive, and not suitable for a wide range of applications. In this study, the relationship between the change rate of the vegetation index obtained by the unmanned aerial vehicle multi-spectrum and the ground boll opening rate was established to realize rapid non-destructive testing of the boll opening rate. The normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) had good prediction ability for the boll opening rate. NDVI in the training set had an R² of 0.912 and rRMSE of 15.387%, and the validation set performance had an R² of 0.929 and rRMSE of 13.414%. GNDVI in the training set had an R² of 0.901 and rRMSE of 16.318%, and the validation set performance had an R² of 0.909 and rRMSE of 15.225%. The accuracies of the models based on GNDVI and NDVI were within the acceptable range. In terms of predictive models, random forests achieve the highest accuracy in predictions. Accurately predicting the cotton boll opening rate can support decision-making for harvest and harvest aid spray timing, as well as provide technical support for crop growth monitoring and precision agriculture.