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.

近年来，计算机视觉概念和信息通信技术的应用已在家庭自动化、医疗保健、智慧城市、精准农业等众多应用中得到观察。物联网 (IoT) 是几乎涵盖所有领域的底层技术。世界基础设施的一部分，其不可或缺的概念是连接每个设备以收集、贡献、体验和分析信息。智能或精准农业以实现农业智能化而闻名。因此，在本文中，已努力从植物叶子中自动检测疾病。对于这个新颖的框架，已经提出了一种名为 IoT_FBFN 的方法，该方法使用启用了物联网的模糊功能网络 (FBFN)。首先，获取叶子的图像。然后对这些图像进行预处理，并使用 Scale-invariant 特征变换方法提取特征。最后，FBFN 用于检测由命名为的昆虫引起的瘿。Pauropsyllatuberculate。网络的训练过程是借助萤火虫算法进行优化，提高了网络的效率。与其他方法相比，所提出的 IoT_FBFN 网络具有模糊逻辑的计算能力和神经网络的学习适应性，对瘿的识别和分类具有更高的准确性。文章最后总结了遇到的挑战和未来的工作。