Journal Article

A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize

Hooda, Karambir Singh, Arora, Alka, Deb, Chandan Kumar, Haque, Md. Ashraful, Misra, Tanuj, Nigam, Sapna, Marwaha, Sudeep

2022

Overview

Maydis leaf blight (MLB) of maize ( Zea Mays L. ), a serious fungal disease, is capable of causing up to 70% damage to the crop under severe conditions. Severity of diseases is considered as one of the important factors for proper crop management and overall crop yield. Therefore, it is quite essential to identify the disease at the earliest possible stage to overcome the yield loss. In this study, we created an image database of maize crop, MDSD (Maydis leaf blight Disease Severity Dataset), containing 1,760 digital images of MLB disease, collected from different agricultural fields and categorized into four groups viz. healthy, low, medium and high severity stages. Next, we proposed a lightweight convolutional neural network (CNN) to identify the severity stages of MLB disease. The proposed network is a simple CNN framework augmented with two modified Inception modules, making it a lightweight and efficient multi-scale feature extractor. The proposed network reported approx. 99.13% classification accuracy with the f1-score of 98.97% on the test images of MDSD. Furthermore, the class-wise accuracy levels were 100% for healthy samples, 98% for low severity samples and 99% for the medium and high severity samples. In addition to that, our network significantly outperforms the popular pretrained models, viz. VGG16, VGG19, InceptionV3, ResNet50, Xception, MobileNetV2, DenseNet121 and NASNetMobile for the MDSD image database. The experimental findings revealed that our proposed lightweight network is excellent in identifying the images of severity stages of MLB disease despite complicated background conditions.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Agricultural land / Agricultural production / Artificial neural networks / Blight / Cereal crops / Computer vision / convolutional neural network / Corn / Crop damage / Crop diseases / Crop management / Crop yield / Crops / Datasets / Deep learning / disease severity stages / Fungal diseases / Fuzzy logic / Identification / Image databases / inception module / Leaf blight / Machine learning / maize crop / maydis leaf blight disease / MDSD image database / Measles / Medical imaging / Neural networks / Plant Science / Rice / Wheat