Densified biochar capsules as an alternative to conventional seedings.

The application of biochar in soil provides various benefits that can vary in intensity as the pyrolysis temperature increases. However, its low density makes this material easily transportable and prone to being removed from the system. The objective of this study was to investigate the pyrolysis temperatures and compression pressure of densified biochar carrier capsules on the physiological quality of Schizolobium parahyba var. amazonicum seeds. Produced at three final pyrolysis temperatures (300, 600, and 900 °C), the biochar was characterized through bulk and true density analyses, immediate composition, pH, electrical conductivity, cation exchange capacity, water-soluble carbon, characterization of organic structures by FTIR, and PAH analysis. Subsequently, the biochar was compacted by briquetting at two compression pressures (50 and 200 psi) with one seed per capsule, and germination, emergence, and quality of generated seedlings were evaluated. After verifying residue normality and variance homogeneity, analysis of variance was conducted following a completely randomized design in a 3 × 2 factorial arrangement, with four replications per treatment and two additional control treatments. Upon identifying significant differences, regression model adjustments were performed. Cluster-based multivariate analysis was used to identify similarities among the studied treatments, both for capsules and controls. Pyrolysis temperature and compression pressure influenced seed germination, emergence, and initial seedling growth. Lower pressure favored shoot development, while higher pressure favored root development and generated seedlings of higher quality. The benefits of biochar to soil, combined with the implementation of seeds, make the production of densified biochar capsules an alternative to conventional seedings, potentially reducing high energy and financial costs and enabling the recovery of degraded areas, even in difficult-to-access regions.

Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging.

Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented. We developed classifier models using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging techniques to predict seed germination and vigor. A forage grass (Urochloa brizantha) was used as a model species. FT-NIR spectroscopy data and radiographic images were obtained from individual seeds, and the models were created based on the following algorithms: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), naive Bayes (NB), and support vector machine with radial basis (SVM-r) kernel. In the germination prediction, the models individually reached an accuracy of 82% using FT-NIR data, and 90% using X-ray data. For seed vigor, the models achieved 61% and 68% accuracy using FT-NIR and X-ray data, respectively. Combining the FT-NIR and X-ray data, the performance of the classification model reached an accuracy of 85% to predict germination, and 62% for seed vigor. Overall, the models developed using both NIR spectra and X-ray imaging data in machine learning algorithms are efficient in quickly, non-destructively, and accurately identifying the capacity of seed to germinate. The use of X-ray data and the LDA algorithm showed great potential to be used as a viable alternative to assist in the quality classification of U. brizantha seeds.

Detection of Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in Black Oat Seeds (Avena strigosa Schreb) Using Multispectral Imaging.

Conventional methods for detecting seed-borne fungi are laborious and time-consuming, requiring specialized analysts for characterization of pathogenic fungi on seed. Multispectral imaging (MSI) combined with machine vision was used as an alternative method to detect Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in black oat seeds (Avena strigosa Schreb). The seeds were inoculated with Drechslera avenae (D. avenae) and then incubated for 24, 72 and 120 h. Multispectral images of non-infested and infested seeds were acquired at 19 wavelengths within the spectral range of 365 to 970 nm. A classification model based on linear discriminant analysis (LDA) was created using reflectance, color, and texture features of the seed images. The model developed showed high performance of MSI in detecting D. avenae in black oat seeds, particularly using color and texture features from seeds incubated for 120 h, with an accuracy of 0.86 in independent validation. The high precision of the classifier showed that the method using images captured in the Ultraviolet A region (365 nm) could be easily used to classify black oat seeds according to their health status, and results can be achieved more rapidly and effectively compared to conventional methods.

Condutividade elétrica em função do teor de água inicial de sementes de amendoim / Electrical conductivity and water content in peanut seeds

O vigor de sementes de amendoim (Arachis hypogaea L.) avaliado pelo teste de condutividade elétrica demonstra estreita relação com o desempenho no campo, mas alguns fatores podem afetar o resultado da condutividade elétrica, sendo um destes o teor de água inicial das sementes. Assim, o objetivo deste trabalho foi avaliar o teor de água ou faixa de umidade da semente mais adequado para a avaliação da condutividade elétrica em sementes de amendoim. Quatro lotes de sementes da cultivar 'IAC Tatu ST' e quatro da cultivar 'IAC Runner 886' foram avaliados quanto ao seu potencial fisiológico e posteriormente o teor de água foi ajustado para 5, 7, 9, 11, 13 e 15 por cento, pelo método da atmosfera úmida. O delineamento foi inteiramente casualizado em esquema fatorial 4×6 (lotes × teores de água) para as sementes de cada cultivar, em quatro repetições e os resultados para o fator teor de água foram submetidos à análise de regressão. A maior relação da condutividade elétrica com vigor das sementes ocorreu naquelas com teor de água entre 9 a 15 por cento, de forma que sementes com 5 a 7 por cento de umidade não devem ser submetidas ao teste de condutividade elétrica, porque os lotes expressam alto padrão de germinação e vigor. A condutividade elétrica de sementes de amendoim é influenciada pelo teor de água e a estabilização dos resultados ocorre quando elas estão com teor de água entre 10 e 14 por cento.

The vigor of peanut seeds (Arachis hypogaea L.) assessed by the conductivity test shows close relationship with performance in the field, but some factors may affect the outcome of the electrical conductivity, being one of that the initial water content of seeds. The objective of this study was to evaluate the seed water content most appropriate for evaluating the seed vigor through electrical conductivity test in peanut. Four seed lots of 'IAC Tatu ST' cultivar and four 'IAC Runner 886' were initially evaluated for their physiological potential and subsequently the seed water content was adjusted to 5, 7, 9, 11, 13 and 15 percent by the method of controlled atmosphere. A completely randomized design with treatments arranged in a 4×6 factorial design (seed lots × seed water content levels) for each cultivar and four replications were used. The results for the factor seed water content were subjected to the analysis regression. The higher relationship of electrical conductivity and seed vigor occurred when the seed lots presents seed moisture content varying from 9 to 15 percent. Seeds with 5-7 percent moisture should not be subjected to electrical conductivity test, because lots express high standard germination and vigor. The electrical conductivity of peanut seeds is influenced by the seed moisture content and the stabilization of the results occurs when the seeds have moisture content between 10 and 14 percent.