AI-PotatoGuard: Leveraging Generative Models for Early Detection of Potato Diseases

Abstract

This paper introduces AI-PotatoGuard, an artificial intelligence (AI) tool which enhances the management of diseases in potatoes through the use of generative models and convolutional neural networks (CNN). In contrast to traditional practices, AI-PotatoGuard is a tool which provides the ability to detect potatoes in the early stages of the disease and also precisely detects the area affected. Through AI-PotatoGuard, it was observed that the conventional approach of identifying the diseases have been surpassed with 95% success observed in terms of getting the detection perfectly right and 85% in terms of getting the detection right at a much earlier stage. Traditional practices lagged with 75% detection right observation and a mere 50% in terms of detecting the disease early on. While traditional methods applied chemicals 2–3 times in practice in an area, the monitoring with AI-PotatoGuard resulted in only 2 out of 6 times in the same area. Hence, efficient and sustainable agriculture is achieved using AI.

Plant phenotyping relevance

ジャガイモ病害の早期検出と罹病領域の定量的特定を目的とするAI画像解析ツールが研究の中心であり、植物の病態を直接評価するフェノタイピング手法に該当する。

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