Multilevel classification of disease in plants with IoT using a hybrid optimisation algorithm

The internet of things (IoT) is a technology developed in most of the world's infrastructure with the requisite concept of connecting every device to collect, contribute, experience, and analyse information. Disease in plants is most commonly noticed by identifying leaves and hence IoT helps in the early detection of diseases. This research paper concentrates on multilevel classification using DL enabled model that is trained by the hybridised optimisation algorithm. Here, DL utilised is LeNet for plant type classification, and SqueezeNet for multiclass plant disease detection. Moreover, the training of these DL models is done by the proposed NMBEO, which is a combination of the Namib beetle optimisation (NBO) algorithm + mayfly algorithm (MA) + bald eagle search (BES) algorithm. Before classifying plant types, features are extracted from plant leaf segmentation. Here, UNet is utilised to segment plant leaves, where UNet is trained by MBEO. Moreover, the anisotropic filtering process is followed for input leaf images obtained from simulated data of the internet of things (IoT).