Android-based Corn Disease Automated Recognition Tool Using Convolutional Neural Network
Journal Title: International Journal of Experimental Research and Review - Year 2023, Vol 30, Issue 1
Abstract
One of the most significant crops in the world today, corn, is under attack from several diseases. Typically, visual review and evaluation are used to identify diseases, but they are regarded as being unreliable. Corn farmers needed an automated disease recognition tool to identify different diseases that affect corn. In this study, a pre-trained convolutional neural network (CNN) was employed to create an android-based recognition tool for recognizing corn diseases. A dataset of healthy corn leaves and three (3) maize diseases—common rust, gray leaf spot, and northern leaf blight—was created using an open-source dataset of Plant Village and field images. The researchers used data augmentation, trained the generated neural network, and put it to the test. The dataset was created using a 75–25 split, trained using the transfer learning concept, then fine-tuned using the VGG-16 CNN model. The CNN model was trained using Tensor flow Keras. The model can identify corn diseases, as evidenced by its accuracy of 93.42 percent and F1-score of 93.53 percent. A mobile application employing the Dynamic System Development methodology (DSDM) was created using the methodology. The trained CNN model file was used to create the android application, which serves as a tool for identifying maize diseases. The produced application was deemed to be extremely compliant according to the participants' assessment of the android application using the ISO 25010 software quality standard, with an overall weighted mean of 4.22. The results show that the participants recognized the CDARS application's potential to offer farmers important information and as an automated corn disease recognition tool that could promote more sustainable and secure food production.
Authors and Affiliations
Grace Dipiao Bulawit, Thelma Domingo Palaoag, Benjamin Enriquez Bulawit Jr.
Pneumonia Detection through Deep Learning: A Comparative Exploration of Classification and Segmentation Strategies
The Convolution Neural Network (CNN) algorithm is one of the most widely used methods for identifying and categorizing lung cancer. This paper covers the most suitable architecture and CNN algorithms for lung cancer and...
An Exemplary Computational Approach to Investigate Lumpy Skin Disease in Indian Cattle
Lumpy Skin Disease (LSD) is a highly consequential infectious ailment that affects cattle caused by the Lumpy Skin Disease Virus (LSDV), which is a DNA virus classified under the Capripoxvirus genus of the Poxviridae fam...
GLSTM: A novel approach for prediction of real & synthetic PID diabetes data using GANs and LSTM classification model
Generative Adversarial Network (GAN) is a revolution in modern artificial systems. Deep learning-based Generative adversarial networks generate realistic synthetic tabular data. Synthetic data are used to enhance the siz...
A Co-occurrence Network Analysis of research work in supply chain finance and corporate sustainable strategy in Industrial sector
With the increasing significance of supply chain finance and corporate strategies in industrial settings, this study attempts in implementing bibliometric analysis coupled with co-occurrence network visualization as its...
First record of tail bifurcation in Tokey Gecko (Gekko gecko) from the Kaziranga, Assam, India : a field observation
The Tokay Gecko (Gekko gecko) is the second largest surviving Gecko species and are distributed across much of South-East Asia, Southern China and Northeastern India and Nepal. In Kaziranga landscape Tokay Gecko are fair...