Leaf Disease Classification in Smart Agriculture Using Deep Neural Network Architecture and IoT

Abstract

The Internet of Things (IoT) is bringing a new dimension to the smart farming market. This helps the user to collect the data from the agricultural fields in real time and move it to remote areas for processing. With the available sensor data and the image taken from the fields, automated disease prediction is possible. Deep neural network is used for classification of disease using the leaf images. Agriculture is the backbone of our country, but our output is poor when compared to the global standards due to lack of using technologies in the fields. In this work, various sensors like humidity sensor, pH level monitoring sensor, Temperature sensor, and Soil moisture sensor are used in the agricultural fields for collecting the real-time data. Multiple Sensors are installed in various locations of farms with one common controller Raspberry PI 3 module (RPI3), which was used to control all these sensors. Camera interfacing with RPI can be observed on leaf disease. Convolutional neural network architecture is used for leaf disease detection and classification. The accuracy of the disease classification system using convolutional neural network is 96% when the system is iterated for 50 epochs.