Green Leaf Disease Detection Using CNN Deep Learning Algorithm

IOT Based Intelligent Farming

Peddamadi Bhavana , Dr Bhuvana J

Department of Computer Science & IT

Jain (Deemed to be University)

ABSTRACT

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to developing a plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. A novel way of training and the methodology used to facilitate a quick and easy system implementation in practice. The developed model can recognize three types of diseases of one plant and pesticides and/or fertilizers are advised according to the severity of the diseases. The type of green leaf disease is recognized by CNN. After recognition, the predictive remedy is suggested that can help agriculture-related people and organizations to take appropriate actions against these diseases.

Internet of Things (IoT) is a technology that allows things to communicate and connect with each other. Change the patterns and processes in both industry and agriculture towards higher efficiency. An intelligent farming system (IF) to improve the production process in planting. IF composes of two main parts which are a sensor system and a control system. The control part which watering and roofing systems of an outdoor farm based on the statistical data sensed from the sensor systems. A set of decision rules based on the sensed data is developed to automatically make a decision on whether the watering and roofing system should be on or off. As the energy demand and the environmental problems increase, natural energy sources have become very important as an alternative to conventional energy sources. The renewable energy sector is fast gaining ground as a new growth area for numerous countries.