Authors:
Aditi Palit
and
Kalidas Yeturu
Affiliation:
Department of Computer Science and Engineering,Indian Institute of Technology Tirupati, India
Keyword(s):
Agriculture, Crop Classification, Hyperspectral Imagery, Convolution Neural Network.
Abstract:
Modern smart agriculture utilizes Unmanned Arial Vehicles (UAVs) with hyperspectral cameras to enhance crop production to address the food security challenges. These cameras provide detailed crop information for type identification, disease detection, and nutrient assessment. However, processing Hyper Spectral Image (HSI) is complex due to challenges such as high inter-class similarity, intra-class variability, and overlapping spectral profiles. Thus, we introduce the Agrinet model, a convolutional neural network architecture, to handle complex hyperspectral image processing. Our novelty lies in the image pre-processing step of selecting suitable bands for better classification. In tests, Agrinet achieved an impressive accuracy of 99.93% on the LongKou crop dataset, outperforming the existing methods in classification.