PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification
arXiv:2405.07332v2 Announce Type: cross Abstract: Numerous applications have resulted from the automation of agricultural disease segmentation using deep learning techniques. However, when applied to new conditions, these applications frequently face the difficulty of overfitting, resulting in lower segmentation performance. In the context of potato farming, where diseases have a large influence on yields, it is critical for the agricultural economy to quickly and properly identify these diseases. Traditional data augmentation approaches, such as rotation, flip, and translation, have limitatio
Despite previous limitations in agricultural disease segmentation, the advancement of AI techniques like GANs and Explainable AI is enabling more robust solutions for critical crops.
Improved early and accurate disease identification in agriculture directly mitigates yield losses, enhances food security, and optimizes resource allocation in farming.
The application of advanced AI models can now offer more reliable and adaptable disease detection, moving beyond the overfitting challenges of traditional deep learning methods in agriculture.
- · Agricultural AI developers
- · Potato farmers
- · Food security initiatives
- · Agricultural technology providers
- · Traditional crop disease inspection methods
- · Regions heavily reliant on manual agricultural monitoring
Reduced potato crop losses globally due to more effective disease management.
Increased investment and adoption of AI-driven precision agriculture technologies across various crops.
Potential for new agricultural economic models based on predictive health and optimized yield management, ultimately influencing global food prices and availability.
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Read at arXiv cs.AI