Deepincepnet: Disease Detection in Corn or Maize Plant Leaves Using Specim Iq Hyperspectral Imaging and Proposed Dnn Classifiers with Inception Networks

Abstract

This research proposes DeepIncepNet, a novel method that combines deep neural networks (DNNs) and hyperspectral imaging to identify illnesses in the leaves of corn or maize plants. The Specim IQ system was utilized to gather hyperspectral imaging data, which encompasses a broad range of wavelengths in spectral information. Using a unique DNN architecture, DeepIncepNet uses Inception Networks (InceptionV3) to classify healthy and damaged maize leaves. To assess the performance of the suggested model, it is compared to well-known architectures as InceptionV3, ResNet-50, and ResNet-101. The results of the experiments suggest that DeepIncepNet achieves greater robustness and accuracy in disease identification, highlighting its potential for early detection and treatment of diseases affecting the maize or corn plant.