Light-based methods are being further developed to meet the growing demands for food in the agricultural industry. Optical imaging is a rapid, non-destructive, and accurate technology that can produce consistent measurements of product quality compared to conventional techniques. In this research, a novel approach for seed quality prediction is presented. In the proposed approach two advanced optical imaging techniques based on chlorophyll fluorescence and chemometric-based multispectral imaging were employed. The chemometrics encompassed principal component analysis (PCA) and quadratic discrimination analysis (QDA). Among plants that are relevant as both crops and scientific models, tomato, and carrot were selected for the experiment. We compared the optical imaging techniques to the traditional analytical methods used for quality characterization of commercial seedlots. Results showed that chlorophyll fluorescence-based technology is feasible to discriminate cultivars and to identify seedlots with lower physiological potential. The exploratory analysis of multispectral imaging data using a non-supervised approach (two-component PCA) allowed the characterization of differences between carrot cultivars, but not for tomato cultivars. A Random Forest (RF) classifier based on Gini importance was applied to multispectral data and it revealed the most meaningful bandwidths from 19 wavelengths for seed quality characterization. In order to validate the RF model, we selected the five most important wavelengths to be applied in a QDA-based model, and the model reached high accuracy to classify lots with high-and low-vigor seeds, with a correct classification from 86 to 95% in tomato and from 88 to 97% in carrot for validation set. Further analysis showed that low quality seeds resulted in seedlings with altered photosynthetic capacity and chlorophyll content. In conclusion, both chlorophyll fluorescence and chemometrics-based multispectral imaging can be applied as reliable proxies of the physiological potential in tomato and carrot seeds. From the practical point of view, such techniques/methodologies can be potentially used for screening low quality seeds in food and agricultural industries.

Seeds of high physiological quality are defined by their superior germination capacity and uniform seedling establishment. Here, it was investigated whether multispectral images combined with machine learning models can efficiently categorize the quality of peanut seedlots. The seed quality from seven lots was assessed traditionally (seed weight, water content, germination, and vigor) and by multispectral images (area, length, width, brightness, chlorophyll fluorescence, anthocyanin, and reflectance: 365 to 970 nm). Seedlings from the seeds of each lot were evaluated for their photosynthetic capacity (fluorescence and chlorophyll index, F0, Fm, and Fv/Fm) and stress indices (anthocyanin and NDVI). Artificial intelligence features (QDA method) applied to the data extracted from the seed images categorized lots with high and low quality. Higher levels of anthocyanin were found in the leaves of seedlings from low quality seeds. Therefore, this information is promising since the initial behavior of the seedlings reflected the quality of the seeds. The existence of new markers that effectively screen peanut seed quality was confirmed. The combination of physical properties (area, length, width, and coat brightness), pigments (chlorophyll fluorescence and anthocyanin), and light reflectance (660, 690, and 780 nm), is highly efficient to identify peanut seedlots with superior quality (98% accuracy).

The rapid and uniform establishment of crop plants in the field underpins food security through uniform mechanical crop harvesting. In order to achieve this, seeds with greater vigor should be used. Vigor is a component of physiological quality related to seed resilience. Despite this importance, there is little knowledge of the association between events at the molecular level and seed vigor. In this study, we investigated the relationship between gene expression during germination and seed vigor in soybean. The expression level of twenty genes related to growth at the beginning of the germination process was correlated with vigor. In this paper, vigor was evaluated by different tests. Then we reported the identification of the genes Expansin-like A1, Xyloglucan endotransglucosylase/hydrolase 22, 65-kDa microtubule-associated protein, Xyloglucan endotransglucosylase/hydrolase 2, N-glycosylase/DNA lyase OGG1 and Cellulose synthase A catalytic subunit 2, which are expressed during germination, that correlated with several vigor tests commonly used in routine analysis of soybean seed quality. The identification of these transcripts provides tools to study vigor in soybean seeds at the molecular level.

Aims To characterise the temporal variability in soil CO 2 emissions (FCO 2 ), soil O 2 influx (FO 2 ), soil water content (SWC), and soil temperature (Ts) and their relations in long-term reforested areas (30 years of conversion) in an Oxisol, Cerrado biome, Brazil. Methods The following land-use changes (Luces) were evaluated: pine (PI), eucalyptus (EU), and native species (NS) reforested areas. The molar ratio between FCO 2 and FO 2 (respiratory quotient, RQ) was calculated to better understand the process of soil metabolism. Results Soil CO 2 emission was 28% less in PI than in the other LUCs. A model including Ts, SWC, and FO 2 could explain 91 and 62% of the FCO 2 temporal variability in NS and PI, respectively. The total FCO 2 (November 2015 to May 2016) were 11.26, 10.99, and 7.97 Mg ha-1 for EU, NS, and PI areas, respectively (p < 0.05). The SWC, but not Ts, influenced the Plant Soil

Radiographic and multispectral image analysis have potential to be efficient, objective methods for assessing seed quality and internal insect infestation. The aim of this study was to verify the efficiency of radiographic and multispectral analysis in detecting signs and damage caused by Angoumois grain moth [Sitotroga cerealella (Olivier)] and its different developmental stages in wheat (Triticum aestivum L.) seeds. The experiment was conducted in a completely randomized design with six replications of 50 seeds. The samples were subjected to laboratory-induced infestation and after 5 and 10 d, radiographic and multispectral analysis were conducted. Afterwards, the seeds were immersed in water for 24 h and then sectioned with a cutting blade. The number of seeds with signs of eggs or oviposition, larvae, pupae, adult insects and insect galleries was quantified. The generalized linear models (GLM) methodology was used and the Tukey test (p < .05) was used to compare the means. In general, the radiographic (with or without contrast) and multispectral methods are viable tools to evaluate insect-infested and uninfested wheat seeds. Multispectral analysis was efficient only in identifying eggs on the seed surface and does not detect the presence of larvae and pupae inside the seeds.