An Early and Smart Detection of Corn Plant Leaf Diseases Using IoT and Deep Learning Multi-Models

Abstract

Plant leaf diseases have various causes, leading to severe disorders. The early and accurate detection and classification of these diseases are fundamental for fostering healthy crop production. In recent years, smart agricultural systems have garnered significant attention due to their capability to enhance efficiency by deploying sensor networks and Internet of Things (IoT) devices that collect and analyze environmental data. However, traditional plant disease detection methods are manual, time-consuming, and often need help handling the data’s complexity and dynamism. These manual methods do not use heterogeneous data to make better decisions. Corn holds significant importance yet it faces numerous diseases that include main three diseases such as blight, common rust, and grey leaf spot. The advancement of computer technology has led to a pivotal focus on corn leaf diseases classification application based on deep learning. Convolutional Neural Networks (CNNs) have revealed remarkable achievements within Precision Agriculture (PA) due to their ability to enhance information. To this end, this work introduces a CNN-based architecture, the Multi-Model Fusion Network (MMF-Net). Its primary objective is to classify diseases within the realm of PA. MMF-Net integrates multi-contextual features using RL-block and PL-blocks 1 & 2, thus effectively combining different model streams trained on heterogeneous data. The RL-block uses spatial range to process coarse grained images to convolve the local context, while PL-block 1 extracts fine-grained global context by expanding the perceptual area of images. PL-block 2 deals with real-life environmental parameters as features. The extracted features are syndicated using multiple classifiers that ensemble three individual blocks at the decision level to improve the accuracy. After fusion, it uses adaptively the majority voting scheme to generate the final decision probability score of the base model. Multiple experiments are conducted involving the corn leaf diseases dataset and a real-life numerical dataset, generating an impressive 99.23% accuracy in the classification of corn leaf diseases. Overall, MMF-Net provides a promising and smart solution to identify plant leaf diseases in PA effectively.

Keywords

• Precision agriculture
• corn disease
• sensors
• pest control
• CNN
• decision level fusion