Most rice growth stage predictions are currently based on a few rice varieties for prediction method studies, primarily using linear regression, machine learning, and other methods to build growth stage prediction models that tend to have poor generalization ability, low accuracy, and face various challenges. In this study, multispectral images of rice at various growth stages were captured using an unmanned aerial vehicle, and single-plant rice silhouettes were identified for 327 rice varieties by establishing a deep-learning algorithm. A growth stage prediction method was established for the 327 rice varieties based on the normalized vegetation index combined with cubic polynomial regression equations to simulate their growth changes, and it was first proposed that the growth stages of different rice varieties were inferred by analyzing the normalized difference vegetation index growth rate. Overall, the single-plant rice contour recognition model showed good contour recognition ability for different rice varieties, with most of the prediction accuracies in the range of 0.75–0.93. The accuracy of the rice growth stage prediction model in recognizing different rice varieties also showed some variation, with the root mean square error between 0.506 and 3.373 days, the relative root mean square error between 2.555% and 14.660%, the Bias between1.126 and 2.358 days, and the relative Bias between 0.787% and 9.397%; therefore, the growth stage prediction model of rice varieties can be used to effectively improve the prediction accuracy of the growth stage periods of rice.

The makapuno coconut endosperm is distinguished by its soft and irregular texture, in contrast to the solid endosperm of regular coconuts. To establish a scientific foundation for studying makapuno coconuts, callus was induced from makapuno endosperm using a combination of plant growth regulators. The induction was successful, and the resulting callus was subsequently subcultured for further study. Transcriptome sequencing of the makapuno callus identified 429 differentially expressed genes (DEGs), with 273 upregulated and 156 downregulated, compared to callus derived from regular coconut endosperm. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that these DEGs were involved in key metabolic pathways, including fructose and mannose metabolism, carbon fixation in photosynthetic organisms, galactose metabolism, and amino sugar and nucleotide sugar metabolism. Furthermore, lipid content analysis of the makapuno callus revealed a significantly higher total lipid level compared to regular callus, with notable differences in the levels of specific fatty acids, such as myristic acid, palmitic acid, and linoleic acid. This study establishes a novel platform for molecular biological research on makapuno coconuts and provides valuable insights into the molecular mechanisms underlying the formation of makapuno callus tissue. The findings also lay the groundwork for future research aimed at elucidating the unique properties of makapuno endosperm and exploring its potential applications.

Tissue culture technology represents a promising strategy for addressing the supply constraints of Areca catechu seedlings. Significant differences in embryoid proliferation were observed between isolated (CK) and aggregated (GL) culture conditions during subculture. To elucidate the underlying mechanisms, transcriptomic analysis was performed. Growth analysis indicated that GL embryoids exhibited the highest growth rate (50.2%) between days 12 and 15, with a peak proliferation efficiency of 50.52%. KEGG analysis identified plant hormone signaling as a key pathway. ELISA quantification demonstrated consistently higher JA levels in CK embryos, peaking at 294.06 ng/g on day 15, while IAA levels were significantly elevated in GL embryos (46.42 ng/g on day 15). The transcription factor AcGIF1 was identified as a central regulator, with further experiments confirming that JA negatively regulates and IAA positively regulates its expression. This study provides critical insights into the molecular mechanisms governing embryoid proliferation in response to inoculation density.

Coconut (Cocos nucifera L.) is an important tropical horticultural crop valued for its edible endosperm with high oil content. However, the molecular mechanisms governing carbon partitioning and the regulation of glycolytic genes during endosperm development remain largely unclear. Fructose-1,6-bisphosphate aldolase (FBA), a key enzyme in glycolysis, plays a central role in carbohydrate metabolism, yet its transcriptional regulatory mechanisms in coconut have not been elucidated. In this study, the CoFBA promoter (proFBA) was isolated and used for yeast one-hybrid screening, leading to the identification of a GATA transcription factor, CoGATA2. Subcellular localization analysis confirmed that CoGATA2 is localized in the nucleus. Yeast one-hybrid assays, electrophoretic mobility shift assays (EMSA), and transient expression in coconut protoplasts demonstrated that CoGATA2 directly binds to a proFBA fragment containing the predicted GATA motif and represses CoFBA expression. Moreover, virus-induced gene silencing (VIGS) of CoGATA2 in coconut callus resulted in significant upregulation of CoFBA expression and increased fructose-1,6-bisphosphate (FBP) levels. Collectively, these findings demonstrate that CoGATA2 functions as a transcriptional repressor of CoFBA and that this regulation correlates with altered FBP levels in coconut callus. However, the precise mechanism by which FBP accumulation occurs and its relationship to glycolytic flux require further investigation. This study provides new insights into the transcriptional regulation of a key glycolytic gene in coconut and offers a foundation for future efforts to manipulate carbon partitioning in this important horticultural crop.