Multimodal Edge Intelligence for Crop Disease Detection and Irrigation Advisory in Precision Agriculture

Abstract

Crop losses caused by disease, water stress, and delayed field intervention remain a major challenge for small and medium farmers. Conventional advisory systems often depend on manual inspection or cloud-only diagnosis, which can be slow in rural environments where connectivity is limited. This paper proposes a multimodal edge-intelligence framework that combines leaf-image analysis, soil-moisture sensing, weather context, and lightweight decision rules to provide early crop disease detection and irrigation advisory. The system uses a compact convolutional neural network for visual symptoms and a sensor-fusion module for environmental risk estimation. By running inference near the field, the framework reduces latency and protects farm data while still supporting periodic cloud synchronization. Simulated evaluation shows 91.8% disease classification accuracy, 16.4% water saving, and faster advisory delivery compared with image-only and rule-based baselines.

Plant phenotyping relevance

葉画像から作物の病徴・病害状態を推定するCNNとセンサ融合手法の開発・評価が中心であり、植物状態の表現型計測に該当する。灌漑助言部分も含むが、病害検出手法が明示的に評価されている。

Code and data availability

The paper describes a simulated evaluation with no public dataset, code, model, or supplement availability statement. The dataset is described only as simulated ('A simulated dataset was prepared using crop categories commonly cultivated in northern India'), with no repository, DOI, or author-provided URL for any asset

No evidence-backed public reproduction asset is currently recorded.