Terpenes play a vital role in plant defense; tomato plants produce a diverse range of terpenes within specialized glandular trichomes, influencing interactions with herbivores, predators, and pollinators. This study employed two distinct methods, namely leaf dip and maceration, to extract trichomes from tomato leaves. Terpene quantification was carried out using Gas Chromatography -Mass Spectrometry (GC -MS). The leaf dip method proved effective in selectively targeting trichome content, revealing unique extraction patterns compared to maceration. The GC -MS method demonstrated high linearity, accuracy, sensitivity, and low limits of detection and quantification. Application of the method to different tomato species ( Solanum pennellii, Solanum pimpinellifolium, Solanum galapagense, Solanum habrochaites , and Solanum lycopersicum ) identified significant variation in terpene content among these species, highlighting the potential of specific accessions for breeding programs. Notably, the terpene alpha-zingiberene, known for its repellency against whiteflies, was found in high quantities (211.90 -9155.13 mu g g -1 ) in Solanum habrochaites accession PI209978. These findings provide valuable insights into terpenoid diversity for plant defense mechanisms, guiding future research on developing pest -resistant tomato cultivars. Additionally, the study underscores the broader applications of terpenes in agriculture.

The challenges that sweet potato whitefly (Bemisia tabaci) creates for vegetable production have increased in the southeastern U.S. Growers must use intensive insecticide spray programs to suppress extremely high populations during the fall growing season. Thus, the objective of this study was to evaluate the use of a reflective plastic mulch and an insect row cover as alternative methods to the current grower practices to manage whiteflies in zucchini (Cucurbita pepo) production. Field experiments were conducted with a two-level factorial experimental design of cover and plastic mulch treatments arranged in a randomized complete block design, with four replications in Georgia in 2020 and 2021, and in Alabama in 2021. Cover treatments consisted of an insect row cover installed on zucchini beds at transplanting and removed at flowering and a no-cover treatment, while plastic mulch treatments consisted of reflective silver plastic mulching and white plastic mulching. During all growing seasons, weather conditions were monitored, whitefly populations were sampled weekly, zucchini biomass accumulation was measured at five stages of crop development, and fruit yield was determined at harvesting. Warm and dry weather conditions early in the growing season resulted in increased whitefly populations, regardless of location and year. In general, the reflective silver plastic mulching reduced whitefly populations compared to the conventional white plastic by 87% in Georgia in 2020, 33% in Georgia in 2021, and 30% in Alabama in 2021. The insect row cover treatment reduced whitefly populations to zero until its removal. Consequently, zucchini plants grown with the insect row cover and reflective silver plastic mulching had an increased rate of biomass accumulation due to the lower insect pressure in all locations. Zucchini grown using silver reflective plastic mulch and row covers had an overall increase of 17% and 14% in total yield compared to white plastic mulch and no-cover treatments, respectively. Significant differences in yield among locations were likely due to severe whitefly pressure early in the fall season, and total yields in Georgia in 2020 (11,451 kg ha⁻¹) were 25% lower than in Georgia in 2021 (15,177 kg ha⁻¹) and in Alabama in 2021 (15,248 kg ha⁻¹). In conclusion, silver plastic mulching and row covers reduced the whitefly population and increased biomass accumulation and total yield. These treatments can be considered ready-to-use integrated pest management practices for growers.

Strawberries are produced in tropical regions using imported cultivars adapted to temperate and subtropical climates. These cultivars, under tropical conditions, produce below their genetic potential. Through multivariate analyses, the objective was to evaluate and select short-day strawberry genotypes based on intraspecific crosses, product characteristics, and fruit quality. The genotypes were obtained from the cross between ‘Camino Real’ (female parent) and the first-generation genotypes RVCA16, RVCS44, RVFS06, RVFS07, and RVDA11 (male parent), obtained in previous selections. The experimental design consisted of augmented blocks with standard controls, consisting of first-generation genotypes and commercial cultivars. The fruits were harvested and evaluated for productivity and post-harvest characteristics: total fruit mass (MTF), total number of fruits (TFN), average fruit mass (AFM), commercial fruit mass (CFM), fruit commercial number (CFN), average commercial mass of fruits (ACFM), total soluble solids (TSS), firmness (F), brightness (L), hue angle (°Hue), and chroma (C). The selection index of Mulamba and Mock (1978) was used with an intensity of 3% to obtain superior genotypes and submitted to multivariate analysis for comparative purposes. Of the 1500 genotypes evaluated, it was possible to select 44 genotypes with characteristics superior to the 13 controls. The RVDA11CR59 genotype showed better values for the attributes of interest, but the RVCS44CR population, from the cross between ‘Camino Real’ × RVCS44 (‘Camarosa’ × ‘Sweet Charlie’), obtained the highest number (16) of individuals among those selected. Significant traits had high heritability but were not necessarily reflected in high selection gain. Coefficients of genetic variation were high, indicating sufficient genetic variability to select genotypes for these traits. When multivariate analyses were used, it was possible to group the selected genotypes into the same cluster according to the similarity and balance in the responses to the evaluated variables, demonstrating that these analyses help other parameters choose superior genotypes. The multivariate analysis allowed the selection of more balanced genotypes for production and post-harvest traits for tropical climates.

The nutritional and commercial value of lettuce (Lactuca sativa L.) is determined by its foliar pigment and phenolic composition, which varies among cultivars. This study aimed to assess the capacity of hyperspectral and applied multispectral imaging, combined with machine learning algorithms, to predict and map key biochemical traits, such as chloroplastidic pigments (chlorophylls and carotenoids) and extrachloroplastidic pigments (anthocyanins, flavonoids, and phenolic compounds). Eleven cultivars exhibiting contrasting pigmentation profiles were grown under controlled greenhouse conditions, and their chlorophyll a and b, carotenoid, anthocyanin, flavonoid, and total phenolic contents were evaluated. Spectral reflectance data were acquired via a Headwall hyperspectral sensor and a MicaSense multispectral sensor, and the pigment contents were quantified via solvent extraction and a UV microplate reader. We developed predictive models via seven machine learning approaches, with partial least squares regression (PLSR) and random forest (RF) emerging as the most robust algorithms for pigment estimation. Chlorophyll a and b are highly and positively correlated (r > 0.9), which is consistent with their hyperspectral reflectance imaging results. The hyperspectral data consistently outperformed the multispectral data in terms of predictive accuracy (e.g., R² = 0.91 and 0.76 for anthocyanins and flavonoids via RF) and phenolic compounds with R² = 0.79, capturing subtle spectral features linked to biochemical variation. Spatial maps revealed strong genotype-dependent heterogeneity in pigment and phenolic distributions, supporting the potential of this approach for cultivar discrimination and pigment phenotyping. These findings demonstrate that hyperspectral imaging integrated with data-driven modelling offers a powerful, nondestructive framework for the biochemical monitoring of leafy vegetables, supporting breeding, precision agriculture, and food quality assessment.

Strawberry is produced in tropical regions using imported cultivars adapted to temperate and subtropical climates. These cultivars, under tropical conditions, produce below their genetic potential. Through multivariate analyses, the objective was to evaluate and select short-day strawberry genotypes based on intraspecific crosses, product characteristics, and fruit quality. The genotypes were obtained from the cross between 'Camino Real' (female parent) and the first-generation genotypes RVCA16, RVCS44, RVFS06, RVFS07, and RVDA11 (male parent), obtained in previous selections. The experimental design consisted of augmented blocks with standard controls, consisting of first-generation genotypes and commercial cultivars. The fruits were harvested and evaluated for productivity and post-harvest characteristics: Total Fruit Mass (MTF); Total Number of Fruits (TFN); Average Fruit Mass (AFM); Commercial Fruit Mass (CFM); Fruit Commercial Number (CFN); Average Commercial Mass of Fruits (ACFM); Total Soluble Solids (TSS); Firmness (F); Brightness (L); Hue Angle (°Hue); Chroma (C). The selection index of Mulamba and Mock (1978) was used with an intensity of 3% to obtain superior genotypes and submitted to multivariate analysis for comparative purposes. Of the 1500 genotypes evaluated, it was possible to select 44 genotypes with characteristics superior to the 13 controls. The RVDA11CR59 genotype showed better values for the attributes of interest, but the RVCS44CR population, from the cross between 'Camino Real' × RVCS44 ('Camarosa' × 'Sweet Charlie'), obtained the highest number (16) of individuals among those selected. Significant traits had high heritability but were not necessarily reflected in high selection gain. Coefficients of genetic variation were high, indicating sufficient genetic variability to select genotypes for these traits. When multivariate analyses were used, it was possible to group the selected genotypes into the same cluster according to the similarity and balance in the responses to the evaluated variables, demonstrating that these analyses help other parameters choose superior genotypes. The multivariate analysis allowed the selection of more balanced genotypes for production and post-harvest traits for tropical climates.