The study assessed machine and deep learning algorithms’ ability to predict and classify the quality of maize grain seed
for increased agricultural output. It relied on a dataset of 2460 maize seed samples examined by a KEPHIS ISTA-accredited
seed testing facility. The K-NN and Logistic Regression algorithms performed the best in predicting and classifying seed
samples, with 100% accuracy, precision, recall, and fi-score. The algorithms found that 46.2% of the grain maize seed was
correctly classified as poor-quality seed due to improper handling, and poses a danger to productivity and food security
for smallholder farmers. The Deep Learning Convolutional Neural Network presented a 92% accuracy with slight fluctuations,
mainly due to the simple and structured nature of the data, which was not in a grid-like or time series format. The
study therefore recommends using K-Nearest Neighbor and/or Logistic Regression for grain seed classification when
presented with well-structured agricultural data. Still, it also suggests expanding the methodology to other agricultural
commodities and implementing seed management measures to prevent low-quality seed distribution. This includes
training traders on how to maintain ISTA-required levels of germination, purity, and moisture content in their stores. The
study highlights the significance of high-quality seeds for smallholder farmers to improve production and food security.