A multimodal fusion framework to diagnose cotton leaf curl virus using machine vision techniques

Abstract

Cotton diseases are disastrous for quality and sustainable production of the yield. Cotton leaf curl virus (CLCuV) is one of the most damaging diseases for cotton crops. Symptoms-based CLCuV identification is tedious, time consuming, error prone and needs exceptional expertise. Sensor-based machine vision approaches have great potential to detect the CLCuV at early stages. This research study proposes a machine vision-based multimodal fusion framework to diagnose various CLCuV severity levels. Our designed model is based on three contrasting datasets: digital photographic, multispectral and fused datasets. A digital camera was used to acquire the digital photographic dataset, the multispectral dataset was obtained by a multispectral radiometer-5 (MSR5), and the two datasets were fused to formulate the third one. From the digital photographic dataset, 269 texture features were extracted and optimized to the most discriminant 30 texture features, the multispectral dataset consisted of 5 spectral features, and the fused dataset was formed by combining the two. The 30 most discriminant features from the digital photographic dataset were selected by incorporating fisher co-efficient, probability of error plus average correlation and mutual information (MI). To diagnose CLCuV, four machine-learning classifiers, namely simple logistics (SL), multilayer perceptron (MLP), sequential minimal optimization (SMO), and random forest (RF), were deployed separately on each dataset. The maximum CLCuV diagnosing accuracies attained from digital photographic, multispectral, and fused datasets were 81.263%, 91.177%, and 96.313%, respectively.

Keywords:

• MSR5
• CLCuV
• Machine-learning classifiers
• MLP
• SL

REVIEWING EDITOR:

• Manuel Tejada, Universidad de Sevilla, Spain

SUBJECTS:

• Artificial Intelligence
• Computation
• Databases
• Testing

Disclosure statement

The authors declare no conflitcs of interest.

Additional information

Notes on contributors

Nazir Ahmad

Mr. Nazir Ahmad is Assistant Professor and Ph.D research scholar in the Department of Information Technology, The Islamia University of Bahawalpur. He is an esteemed teacher, having good knowledge in pattern recognition, data mining, machine learning and artificial intelligence. His research focuses on machine learning application in agriculture and health care. Mr. Nazir Ahmad has worked as a co-author in many publications, and supervised almost 10 MPhil students. He also has supervised more than 100 undergraduate students. He is recognized for his commitment to education. He fosters student’s growth in technology innovations.