Deep Learning Based Plant Disease Detection

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 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify crop species and diseases (or absence thereof). 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.

Keywords: technology, crop disease, smartphones, image, infrastructure

Cite this Article: Soumya Shetty, Palvi Sawant, Bhagyashree Singasane, Mahi Khemchandani. Deep Learning Based Plant Disease Detection. Journal of Network Security. 2020; 8(2): 33–42p.