ABSTRACT The ranking of seed lots is a fundamental process for all companies in the seed industry. This work aims to demonstrate data mining methods for ranking sorghum seed lots during the seed processing through analysis of quality control data. Germination and cold tests were performed to verify the physiological quality of the lots. Seed samples from each lot were evaluated in two moments: post-cleaning and finished product (ready for marketing). The results after pre-processing totaled 188 rows of data with six attributes, encompassing 150 lots accepted for marketing, 6 rejected, and 32 intermediate lots. The classifiers used were J48, Random Forest, Classification Via Regression, Naive Bayes, Multilayer Perceptron, and IBk. The Resample filter was used for adjustment of the data. The k-fold technique was used for training, with ten folds. The metrics of Accuracy, Precision, Recall, F-measure, and ROC Area were used to verify the accuracy of the algorithms. The results obtained were used to determine the best machine-learning algorithm. IBk and J48 presented the highest accuracy of data; the IBk technique presented the best results. The Resample filter was essential for solving the data imbalance problem. Sorghum seed lots can be classified with great accuracy and precision through artificial intelligence and machine learning technique.

RESUMO A classificação de lotes de sementes é um processo fundamental para todas as empresas do setor sementeiro. O objetivo do trabalho é demonstrar os métodos de mineração de dados de ranqueamento de lotes de sementes de sorgo durante o processo de beneficiamento, através de análises de dados do controle de qualidade. Os testes realizados foram germinação e teste de frio, com o objetivo de verificar a qualidade fisiológica dos lotes. As amostras de sementes de cada lote foram avaliadas em dois momentos: póslimpeza e produto acabado (pronto para comercialização). Os dados gerados, após o pré-processamento, totalizaram 188 linhas com seis atributos, contabilizando 150 lotes aceitos para comercialização, seis rejeitados e 32 denominados intermediários. Os classificadores utilizados foram J48, Random Forest, Classification Via Regression, Naive Bayes, Multilayer Perceptron e IBk. Utilizou-se o filtro Resample para ajustamento dos dados. A técnica empregada para treinamento foi a k-fold, com 10 folds. Para verificar a precisão dos algoritmos foram utilizadas as métricas de Acurácia, Precisão, Recall, F-measure e Área ROC. Com os resultados obtidos determinou-se o melhor algoritmo de aprendizagem de máquina. Verificou-se que o IBk e o J48 obtiveram maior acurácia nos dados, sendo que a técnica de IBk obteve o melhor resultado. O filtro Resample foi importante para resolver o problema do desequilíbrio dos dados. Concluímos ser possível classificar lotes de sementes de sorgo com grande acurácia e precisão através de inteligência artificial e sua técnica de aprendizado de máquina.

ABSTRACT To enhance speed and agility in interpreting physiological quality tests of seeds, The use of algorithms has emerged. This study aimed to identify suitable machine learning models to assist in the precise management of seed lot quality. Soybean lots from two companies were assessed using the Supplied Test Set, Cross-Validation (with 8, 10, and 12 folds), and Percentage Split (with 66% and 70%) methods. Variables analyzed through Tetrazolium tests included vigor, viability, mechanical damage, moisture damage, bed bug damage, and water content. Method performance was determined by Kappa, Precision, and ROC Area metrics. Classification Via Regression and J48 algorithms were employed. The technique utilizing 66% of data for training achieved 93.55% accuracy, with Precision and ROC Area reaching 94.50% for the J48 algorithm. Applying the cross-validation method with 10 folds resulted in 90.22% of correctly classified instances, with a ROC Area outcome like the previous method. Tetrazolium Vigor was the primary attribute used. However, these results are specific to this study's database, and careful planning is necessary to select the most effective application methods.

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.

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.

ABSTRACT The seed germination and vigor evaluation are essential for the sowing sector to measure the performance of different seed lots and improve the efficiency of storage and sowing processes. However, the analysis of various tests to determine seed quality generates a large amount of information, making it almost impossible for humans to perform a quick and effective quality control analysis. Therefore, the objective of this study was to evaluate the differences in the physiological quality of soybean seeds in different cultivars using machine learning techniques to rank the lots based on their quality. Three cultivars were used, and the analysis was germination, accelerated aging, tetrazolium treatment, seedling emergence, and 1000 seed weight from 65 lots were measured. The lots were evaluated in two phases, one immediately after harvest and the other after six months of storage. Random forest, multi-layer perceptron, J48, and classification via regression classifiers were used, aided by the feature resampler technique. Random forest and classification via regression obtained the highest accuracy, and the random forest technique obtained the best results. Therefore, it is possible to classify soybean seed lots with great accuracy and precision using artificial intelligence and machine learning techniques.