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Hyperspectral imaging (HSI) is a non-destructive and contactless technologythat provides valuable information about the structure and composition of anobject. It can capture detailed information about the chemical and physicalproperties of agricultural crops. Due to its wide spectral range, compared withmultispectral- or RGB-based imaging methods, HSI can be a more effective toolfor monitoring crop health and productivity. With the advent of this imagingtool in agrotechnology, researchers can more accurately address issues relatedto the detection of diseased and defective crops in the agriculture industry.This allows to implement the most suitable and accurate farming solutions, suchas irrigation and fertilization before crops enter a damaged anddifficult-to-recover phase of growth in the field. While HSI provides valuableinsights into the object under investigation, the limited number of HSIdatasets for crop evaluation presently poses a bottleneck. Dealing with thecurse of dimensionality presents another challenge due to the abundance ofspectral and spatial information in each hyperspectral cube. State-of-the-artmethods based on 1D- and 2D-CNNs struggle to efficiently extract spectral andspatial information. On the other hand, 3D-CNN-based models have shownsignificant promise in achieving better classification and detection results byleveraging spectral and spatial features simultaneously. Despite the apparentbenefits of 3D-CNN-based models, their usage for classification purposes inthis area of research has remained limited. This paper seeks to address thisgap by reviewing 3D-CNN-based architectures and the typical deep learningpipeline, including preprocessing and visualization of results, for theclassification of hyperspectral images of diseased and defective crops.Furthermore, we discuss open research areas and challenges when utilizing3D-CNNs with HSI data.

smart agricultural technology

A comprehensive review of 3D convolutional neural network-based  classification techniques of diseased and defective crops using non-UAV-based  hyperspectral images