Disease Detection in Corn or Maize Plant Leaves Using Specim Iq Hyperspectral Imaging and Proposed Dnn Classifiers with Alexnet

Abstract

It is essential to identify illnesses in corn and maize plants early on in order to preserve crop health and guarantee agricultural output. This work investigates the detection of plant diseases in leaves using advanced deep learning techniques in conjunction with Specim IQ hyperspectral imaging. We compare the performance of a newly constructed classifier, DeepIncepNet, with other state-of-the-art models, such as InceptionV3, ResNet-50, and ResNet-101. We also present a novel Deep Neural Network (DNN) classifier based on the AlexNet architecture. Preprocessing was done on hyperspectral imaging data to improve image quality and retrieve pertinent characteristics. A large dataset was used to train and verify the classifiers, and the results showed excellent disease detection accuracy. The comparative analysis illustrates the benefits and drawbacks of each model, highlighting the possibility for accurate and effective plant disease diagnosis through the combination of deep learning and hyperspectral imaging—a major improvement over conventional techniques.