One Channel is All You Need: Optimizing Hyperspectral Data for Crop Disease Detection

Fusarium Head Blight (FHB), among other crop diseases, is responsible for major yield losses in small grains. As such, methodologies to detect these diseases from unmanned aerial vehicle (UAV) drones equipped with hyper-spectral sensors have been developed as a time and cost-effective solution for identifying these diseases. The International Conference on Pattern Recognition (ICPR) 2024 held a Kaggle competition to identify the severity of FHB. The dataset provided included 32 × 32 × 101 images for classification, emphasizing the challenging nature of feature extraction and the complex relationship between the channels. A SOTA 100% classification accuracy was reported on the test data using a simple Resnet-inspired architecture using less than three averaged input bands instead of the full 101 bands. This suggests that perhaps an overemphasis is being placed on utilizing all the information from the multiple bands in hyper-spectral imaging (HSI). However, not all well-established techniques transferred this HSI dataset, with little purpose in augmentations or transfer learning. The findings suggest that with change point analysis of the radiance of pixels in the hyper-spectral imagery, one could identify the splits necessary for a significant reduction in input channels via channel-wise normalization followed by simple arithmetic mean averaging across the channels. Suggesting that already well-established CNN architectures are well-suited for crop disease detection from HSI.