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

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.