Automated Plant Leaf Disease Detection and Classification Using Fuzzy Based Function Network

In recent years, the applications of the computer vision concepts and information communication technology has been observed in number of applications including home automation, healthcare, smart cities, precision agriculture etc. Internet of Things (IoT) is the underlying technology that indulges in almost all part of world infrastructure with the indispensable concept of connecting every device for collecting, contributing, experiencing, and analyzing the information. Smart or precision farming is known for achieving intelligence in agriculture. Therefore, in this article, an effort has been made towards automated disease detection from the plant leaves. For this a novel framework, a method named as IoT_FBFN using Fuzzy Based Function Network (FBFN) enabled with IoT has been proposed. At first, the images of leaf are acquired. Then these images are preprocessed and features are extracted using the Scale-invariant feature transform method. Finally, FBFN is used for the detection of the galls caused by the insect named as Pauropsyllatuberculate. The training process of the network is by optimizing with the help of Firefly algorithm, this increases the efficiency of the network. The proposed IoT_FBFN network having the computational power of fuzzy logic and learning adaptability of neural network achieves higher accuracy for identification and classification of galls when compared with the other approaches. The article concludes with the challenges encountered and future works.