Determining Plant Health Using Machine Learning

Abstract

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the
world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration
and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted
disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under
controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or
absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the
feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly
available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global
scale.