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In modern agriculture, Controlled environment agriculture (CEA) stands out as a contemporary production mode that leverages precise control over environmental conditions such as nutrient, temperature, light, and other factors to achieve efficient and high-quality agricultural production. Numerous studies have demonstrated the efficacy of manipulating these environmental factors in the short period before harvest to enhance crop yield and quality in CEA. This comprehensive review aims to provide insight into various pre-harvest practices employed in CEA, including nutrient deprivation, nutrient supply, manipulation of the light environment, and the application of exogenous hormones, with the objective of improving yield and quality in horticultural crops. Additionally, we propose an intelligent pre-harvest management system to cultivate high-quality horticultural crops. This system integrates sensor technology, data analysis, and intelligent control, enabling the customization of specific pre-harvest strategies based on producers’ requirements. The envisioned pre-harvest intelligent system holds the potential to enhance crop quality, increase yield, reduce resource wastage, and offer innovative ideas and technical support for the sustainable development of CEA.

Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.

Accurate and reliable farmland crop mapping is an important foundation for relevant departments to carry out agricultural management, crop planting structure adjustment and ecological assessment. The current crop identification work mainly focuses on conventional crops, and there are few studies on parcel-level mapping of horticultural crops in complex mountainous areas. Using Miaohou Town, China, as the research area, we developed a parcel-level method for the precise mapping of horticultural crops in complex mountainous areas using very-high-resolution (VHR) optical images and Sentinel-2 optical time-series images. First, based on the VHR images with a spatial resolution of 0.55 m, the complex mountainous areas were divided into subregions with their own independent characteristics according to a zoning and hierarchical strategy. The parcels in the different study areas were then divided into plain, greenhouse, slope and terrace parcels according to their corresponding parcel characteristics. The edge-based model RCF and texture-based model DABNet were subsequently used to extract the parcels according to the characteristics of different regions. Then, Sentinel-2 images were used to construct the time-series characteristics of different crops, and an LSTM algorithm was used to classify crop types. We then designed a parcel filling strategy to determine the categories of parcels based on the classification results of the time-series data, and accurate parcel-level mapping of a horticultural crop orchard in a complex mountainous area was finally achieved. Based on visual inspection, this method appears to effectively extract farmland parcels from VHR images of complex mountainous areas. The classification accuracy reached 93.01%, and the Kappa coefficient was 0.9015. This method thus serves as a methodological reference for parcel-level horticultural crop mapping and can be applied to the development of local precision agriculture.

Background MicroRNA (miRNA) is a crucial post-transcriptional regulatory factor in plant growth and development. Duplicated genes often exhibit functional divergence due to competition for the identical miRNA binding sites. Kiwifruit ( Actinidia spp.) is an economically significant horticultural crop renowned for its distinctive flavor, which is largely attributable to elevated citrate levels during fruit development. However, the mechanisms through which miRNA-targeted modules evolve following duplication events and regulate citrate biosynthesis, thereby influencing the unique flavor profile of kiwifruits, remain poorly understood. Results In this study, we examined the expression patterns of miRNAs and interactions with their targets in kiwifruit fruit samples from various pulp tissues and developmental stages. Our analysis identified 46 miRNAs, comprising 44 known miRNAs and two novel/kiwifruit-specific miRNAs, which targeted a total of 1,474 genes. Correlation analysis revealed weak relationships between the expression levels of miRNAs and their target genes. Among these targets, 27 tandemly duplicated genes, and 782 whole genome duplication (WGD) genes exhibited a loss of miRNA binding sites in one of their duplicated copies. Furthermore, weighted gene co-expression network analysis demonstrated that most duplicated genes clustered into distinct gene modules. These findings suggest that the loss of miRNA targets following duplications contributes to expression divergence among gene duplicates, thereby facilitating stable gene expression within the miRNA-targeted network. For instance, the aconitate hydratase genes AchAco4 and AchAco6 were each targeted by different miRNAs, ach-miR-3774 and ach-miR-10371 , respectively. Notably, these genes exhibited distinct expression patterns compared to their duplicated counterparts. Conclusions This study enhances our understanding of how the miRNA- AchAco module regulates citrate content and provides insights into the molecular network that influences the flavor profile of kiwifruit. Clinical trial number Not applicable.

Horticultural production, enriched in essential nutrients such as proteins, carbohydrates, vitamins, and lipids, face challenges due to rapid global population growth, resource shortages, environmental degradation, and a decline in the number of horticultural practitioners. These challenges prevent their yield and quality from meeting sustainability requirements. By regulating temperature, humidity, light, and nutrient supply, plant factories with artificial lighting (PFALs) enable stable crop cultivation, facilitate year-round production, and promote efficient resource use, yielding high-quality horticultural products with significant application potential. However, issues such as high initial investment and electricity costs remain unresolved. Primarily, PFALs are utilized in the production of green leafy vegetables, transplants, and medicinal plants. In PFALs, lettuce yield can increase by over 50% under optimal lighting conditions compared to traditional methods. PFALs are also employed in the commercial production of horticultural seedlings, technical research on shortening growth cycle and mechanism research on environmental regulation of metabolites. This supports rapid breeding and the production of high-value horticultural products. Additionally, this paper addresses the challenges faced by plant factories in horticultural production and explores prospects and considerations for PFAL applications in this domain.

Current work at IL’s lab is funded by grant 2017 SGR 1108 (Generalitat de Catalunya, Catalonia, Spain). DB acknowledges funding from the US-Israeli Binational Agricultural Research Development Fund and the AES Hatch Project CA-D-PLS-2404-H. Work at MD’s lab is funded by National Research Council and National Agency for the Promotion of Scientific and Technological Activities from Argentina.

The quantification of the relationship between morphological and color indicators in various organs of horticultural crops is of great significance for crop digital visualization research using computer vision technology. To study this relationship, observational data from a six-year experiment were collected, focusing on seven kinds of color component values of different organs including root, stem, and leaf. Using the collected color data as input, a simulation model was established based on the Elman neural network for six horticultural crops including zizania, cucumber, celery, spinach, parsley, and tea. Results indicated that the horticultural crop morphology model based on the Elman neural network exhibited high simulation accuracy with root mean square error (RMSE) ranging from 0.14 to 1.05 to and normalized root mean square error (NRMSE) ranging from 2.02% to 11.34% for the maximum root length simulation model. The simulation model for stem length and diameter had an RMSE ranging from 1.42 to 4.96 cm and 0.25 to 1.17 mm, respectively, with NRMSE ranging from 18.19% to 25.65% and 15.13% to 27.25%, respectively. Similarly, chlorophyll content, leaf length, leaf width, and leaf area simulation models exhibited RMSE ranging from 2.80 to 8.22 SPAD, 0.44 to 18.04 cm, 0.22 to 3.49 to, and 0.25 to 36.39 cm2, respectively, with NRMSE ranging from 8.63% to 21.04%, 15.00% to 22.87%, 15.12% to 33.58%, and 6.88% to 24.90%, respectively. These findings provide essential theoretical support for precision agriculture in areas of water and fertilizer management, plant growth diagnosis, and yield prediction.