Early Prediction of Plant Disease ESCA

Abstract

This research revolutionizes grapevine security worldwide and sustains premium wine production by using CNN-driven algorithms and different datasets to pioneer multimodal detection for early Esca disease in grapevines. Also give a model with more accuracy so that we can predict this plant disease early. Global grapevine output is being threatened by the complicated fungal illness known as esca disease, which also threatens the stability of the economy and the quality of premium wines. The capacity of current detection techniques to detect Esca to detect the disease early is restricted and frequently imprecise. By utilizing the capabilities of Convolutional Neural Networks (CNNs) and a variety of datasets, this study offers a novel method that develops multimodal detection for early Esca diagnosis. Compared to traditional methods, our model achieves higher accuracy by combining spectral and temporal information with visual imaging of leaves and stems. Key Words: CNN, ESCA, Machine Learning, Plant Disease

Plant phenotyping relevance

ブドウ樹の葉・茎の画像とスペクトル・時系列情報からEsca病を早期推定するCNNベースのマルチモーダル手法が研究の中心であり、植物の病徴・病害状態を直接推定するため、植物フェノタイピング手法に該当する。

Code and data availability

The paper describes a CNN-based Esca detection study with a manually collected image dataset, but no public dataset, code, model, or supplement with an authors' URL is mentioned. No qualifying paper-specific assets are present.

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