This paper aims to classify macronutrient deficiencies in maize plants using machine learning techniques. Two feature extraction methods are used to generate two feature sets from images of healthy and deficient maize leaves. Various machine learning classifiers including artificial neural networks, support vector machines, k-nearest neighbors, and deep networks with autoencoders are applied to the two feature sets and their classification accuracies are compared. The results show that deep networks with autoencoders achieved the highest accuracy of 100% for one feature set, while k-nearest neighbors performed best for the other feature set. This study demonstrates the effectiveness of machine learning approaches for nutrient deficiency classification in plants.