Deep learning for rapid crop damage assessment after cyclones

In the wake of devastating cyclones, rapid and accurate assessment of crop damage is crucial for timely intervention and resource allocation. The acquiring of high-quality and up-to-date satellite or aerial imagery immediately following a cyclone is often difficult due to adverse weather conditions and limited access to affected areas. The objective of this study is to develop and validate an advanced deep-learning framework capable of rapidly and accurately assessing crop damage caused by cyclones using high-resolution satellite and aerial imagery. Grey-level co-occurrence Matrix (GLCM) is applied to standardize and enhance the contrast of satellite images, improving their suitability. Feature extraction techniques, such as Mixture of Probabilistic Principal Component Analysis (MPPCA), are employed to identify key characteristics indicative of crop damage. The next step is to develop a CNN model to classify crop damage levels based on the extracted features from satellite imagery. The two approaches, Unsupervised Anomaly Detection (UAD) with the GOA-CNN-BiGRU-Attention (GCBA) framework and Generative Adversarial Networks (GANs) for synthetic pre-cyclone imagery offer innovative solutions for rapid and accurate crop damage assessment after cyclones. The findings show the proposed model accuracy appears to attain a higher accuracy of 99%. Future developments in deep learning to quickly estimate agricultural damage following storms could focus on integrating multispectral and hyperspectral imagery for enhanced detection accuracy and incorporating real-time data streams from drones or IoT devices to provide timely and precise assessments during and immediately after cyclonic events.