Explore the vast range of titles available.

Hyperspectral imaging (HSI) has emerged as a powerful tool for non-destructive phenotyping, yet fruit crop applications remain underexplored. We propose a methodological framework to enhance the spectral characterization of apple fruits by identifying robust vegetation indices (VIs) and interpretable endmembers. We screened 284 Vis, which were evaluated using four feature selection algorithms (Boruta, MI+Lasso, RFE, and ensemble voting), generalizing across red, yellow, green, and purple apple cultivars. An ensemble criterion (≥2 algorithms) yielded 50 selected VIs from the NDSI/DSI/RSI families, preserving > 95% classification accuracy and capturing cultivar-specific variation. Pigment-sensitive wavelength bands were identified via PLS-DA VIP scores and one-vs-rest ANOVA. Using these bands, we formulated a new normalized-difference, ratio, and difference spectral indices tailored to cultivar-specific pigmentation. Several indices achieved >89% classification accuracy and showed patterns consistent with those of anthocyanin, carotenoid, and chlorophyll. A two-stage spectral unmixing pipeline (K-Means → N-FINDR) achieved the lowest reconstruction RMSE (0.043%). This multi-level strategy provides a scalable, interpretable framework for enhancing phenotypic resolution in apple hyperspectral data, contributing to fruit index development and generalized spectral analysis methods for horticultural applications.

Rice (Oryza sativa L.) is a staple food for more than half of the global population. Preharvest sprouting (PHS), which reduces yield and grain quality, presents a major challenge for rice production. The development of PHS‐resistant varieties is a major goal in japonica rice breeding. A deep learning model to automate PHS rate measurement was developed using the YOLOv8 algorithm. The model had high mean average precision (0.974). PHS rate measurements made using the model correlated strongly with manual measurements (R2  =  0.9567). A population of 182 F8 recombinant inbred lines (RILs) was derived from a cross between the japonica rice cultivars, Junam and Nampyeong. The RIL genotypes at 763 single nucleotide polymorphism markers were determined using a rice target capture sequencing system and used to create a genetic map. The RILs were cultivated in the field (summer season) and the greenhouse (winter season) and their PHS rates were measured in both environments. Quantitative trait loci (QTLs) associated with PHS were present on chromosomes 3, 6, and 7 in the field, and on chromosomes 1, 2, 3, 6, 7, 8, and 11 in the greenhouse. Three QTLs on chromosomes 3, 6, and 7 showed stable effects in both environments. A search for candidate genes in the QTL qPHS6 identified Os06g0317200. This gene encodes a glycine‐rich protein resembling qLTG3‐1, which controls PHS. The QTLs identified in this study and the deep learning model developed for measuring PHS rates will accelerate the development of rice varieties with enhanced resistance to PHS. Core Ideas Stable quantitative trait loci (QTLs) for preharvest sprouting (PHS) resistance were found on chromosomes 3, 6, and 7. The Os06g0317200, which encodes a glycine‐rich protein, was selected as a candidate gene for the major QTL qPHS6 on chromosome 6. A YOLOv8‐based deep learning model was developed for rice PHS rate measurement. The deep learning model achieved high precision and PHS rate measurements made using the model correlated strongly with manual measurements (R2 = 0.9567). Plain Language Summary Preharvest sprouting (PHS) occurs when seeds germinate on the panicle prior to harvest before harvest due to high humidity and temperature. PHS can reduce crop quality and yield. To understand the genetic basis of PHS resistance in japonica rice, we performed quantitative trait locus (QTL) analysis using a population of recombinant inbred lines derived from a cross between two japonica rice cultivars with different PHS resistance levels. Three QTLs related to PHS with stable effects in different environments were identified on chromosomes 3, 6, and 7. To improve phenotyping efficiency, we developed a deep learning model to measure PHS rates automatically from seed images.

Tiller number is an important trait associated with yield in rice. Tiller number in Korean japonica rice was analyzed under greenhouse conditions in 160 recombinant inbred lines (RILs) derived from a cross between the temperate japonica varieties Odae and Unbong40 to identify quantitative trait loci (QTLs). A genetic map comprising 239 kompetitive allele-specific PCR (KASP) and 57 cleaved amplified polymorphic sequence markers was constructed. qTN3, a major QTL for tiller number, was identified at 132.4 cm on chromosome 3. This QTL was also detected under field conditions in a backcross population; thus, qTN3 was stable across generations and environments. qTN3 co-located with QTLs associated with panicle number per plant and culm diameter, indicating it had pleiotropic effects. The qTN3 regions of Odae and Unbong40 differed in a known functional variant (4 bp TGTG insertion/deletion) in the 5ʹ UTR of OsTB1, a gene underlying variation in tiller number and culm strength. Investigation of variation in genotype and tiller number revealed that varieties with the insertion genotype had lower tiller numbers than those with the reference genotype. A high-resolution melting marker was developed to enable efficient marker-assisted selection. The QTL qTN3 will therefore be useful in breeding programs developing japonica varieties with optimal tiller numbers for increased yield.

Key message Hyperspectral features enable accurate classification of soybean seeds using linear discriminant analysis and GWAS for novel seed trait genes . Evaluating crop seed traits such as size, shape, and color is crucial for assessing seed quality and improving agricultural productivity. The introduction of the SUnSet toolbox, which employs hyperspectral sensor-derived image analysis, addresses this necessity. In a validation test involving 420 seed accessions from the Korean Soybean Core Collections, the pixel purity index algorithm identified seed- specific hyperspectral endmembers to facilitate segmentation. Various metrics extracted from ventral and lateral side images facilitated the categorization of seeds into three size groups and four shape groups. Additionally, quantitative RGB triplets representing seven seed coat colors, averaged reflectance spectra, and pigment indices were acquired. Machine learning models, trained on a dataset comprising 420 accession seeds and 199 predictors encompassing seed size, shape, and reflectance spectra, achieved accuracy rates of 95.8% for linear discriminant analysis model. Furthermore, a genome-wide association study utilizing hyperspectral features uncovered associations between seed traits and genes governing seed pigmentation and shapes. This comprehensive approach underscores the effectiveness of SUnSet in advancing precision agriculture through meticulous seed trait analysis.

Tiller number is an important trait associated with yield in rice. Tiller number in Korean japonica rice was analyzed under greenhouse conditions in 160 recombinant inbred lines (RILs) derived from a cross between the temperate japonica varieties Odae and Unbong40 to identify quantitative trait loci (QTLs). A genetic map comprising 239 kompetitive allele-specific PCR (KASP) and 57 cleaved amplified polymorphic sequence markers was constructed. qTN3, a major QTL for tiller number, was identified at 132.4 cm on chromosome 3. This QTL was also detected under field conditions in a backcross population; thus, qTN3 was stable across generations and environments. qTN3 co-located with QTLs associated with panicle number per plant and culm diameter, indicating it had pleiotropic effects. The qTN3 regions of Odae and Unbong40 differed in a known functional variant (4 bp TGTG insertion/deletion) in the 5ʹ UTR of OsTB1, a gene underlying variation in tiller number and culm strength. Investigation of variation in genotype and tiller number revealed that varieties with the insertion genotype had lower tiller numbers than those with the reference genotype. A high-resolution melting marker was developed to enable efficient marker-assisted selection. The QTL qTN3 will therefore be useful in breeding programs developing japonica varieties with optimal tiller numbers for increased yield.