ABSTRACT Chickpea has recently gained prominence in agriculture, being an excellent option for crop rotation in areas cultivated with soybean or corn. Seeds must have good physiological quality, mainly during storage, to ensure crop success. Seeds undergo metabolic reactions that impair their performance when stored improperly. In this context, a study was carried out to evaluate the physiological variables of the chickpea cultivar BRS Aleppo in different types of packaging and storage environments. The analyses were performed with seeds evaluating water content, germination, first count, and accelerated aging, according to established standards. Therefore, the physiological quality of chickpea seeds can be preserved through controlled storage in hermetic packaging, thus ensuring their viability and vigor over time. agriculture corn success improperly context environments content germination count aging standards Therefore time

RESUMO Analisar os efeitos de dados de secagem em época de safra é primordial para o sucesso do armazenamento, além de manter a qualidade fisiológica das sementes. O objetivo com este trabalho foi avaliar o desempenho de secagem em secadores fixo e móveis através de aprendizado de máquinas. Os dados foram coletados de secadores convectivos, com base em: i) total de secadores trabalhados; ii) tempo de duração de secagem (horas); iii) percentual de umidade na entrada e saída do produto (%); e iv) diferenças de umidade entres ambas (%). Foi utilizado o modelo Filtered Clusterer, baseado na técnica Simple K-Means e filtro Resample, para agrupar dados com base em suas similaridades. Os resultados apresentados demonstram que o sistema de secagem é distinto entre os equipamentos fixo e móvel, com diferenças bem definidas dentro dos sistemas de secagem estabelecidos. O algoritmo juntamente com os filtros demonstrou ser eficiente na classificação não supervisionada, identificando e minimizando similaridade inter-cluster do sistema fixo definindo em classes distintas dentro do conjunto de dados. Conclui-se, que o conjunto de dados dispersos são agrupados de modo que o algoritmo classifica e minimiza similaridade inter-cluster do sistema fixo com alta precisão. Por outro lado, o desempenho da secagem em sistema móvel apresentou baixa eficiência. armazenamento sementes máquinas convectivos i trabalhados ii horas (horas) iii % (%) iv %. . Clusterer KMeans K Means Resample similaridades estabelecidos supervisionada intercluster inter cluster Concluise, Concluise Conclui se, se Conclui-se precisão lado eficiência (horas (% (

ABSTRACT Analyzing the impact of harvest-time drying data is crucial for successful storage and maintaining regulatory seed quality. This study aimed to assess the performance of fixed and mobile dryers using machine learning techniques. Data were collected from convective dryers, including the total number of dryers used, drying time (in hours), moisture percentages at the product’s entrance and exit, and the humidity difference between them. The study employed the Filtered Clusterer model, which utilizes the Simple K-Means technique and the Resample filter to group data based on similarities. The findings indicated distinct differences between fixed and mobile drying systems, with well-defined variations within each system. The algorithm, combined with the applied filters, proved effective in unsupervised classification by identifying and reducing inter-cluster similarity within the fixed system, thereby creating distinct classes within the dataset. In conclusion, the algorithm successfully clustered the scattered dataset and accurately classified and minimized inter-cluster similarity within the fixed system. Conversely, the mobile system exhibited low drying efficiency. harvesttime harvest quality techniques used hours, hours , hours) products product s exit them model KMeans K Means similarities systems welldefined well defined filters intercluster inter cluster conclusion Conversely efficiency