Toward a Smart Agriculture Using Deep Learning for Plant Disease Detection: Challenges and Opportunities

Abstract

Most of the country’s population depends on agriculture. As the world population is constantly growing, food production must also be improved. Plant diseases are a major threat to food security, but their identification is very difficult using traditional techniques. Artificial intelligence, machine learning and deep learning are used in many applications such as agriculture, industries, education and other. Nowadays deep learning algorithms are commonly used in agriculture areas for many applications like soil and water management, crop disease detection, yield prediction, fruit counting, crop cultivation, etc. Smart agriculture is one of IoT’s emerging areas. Controlling water consumption, sensing soil temperature are some of the recognized IoT applications. IoT devices collect agriculture data and deep learning algorithms processes and analyzes these data according agriculture applications. In this paper, we discuss how deep learning is used for smart agriculture, in particular plant disease detection using Convolutional Neural Networks algorithm.