A SMART ROBOT SYSTEM TO PROVIDE ASSISTANCE TO FARMERS ON THE FIELD USING MACHINE LEARNING AND MOBILE INTEGRATION

Abstract

California agriculture faces the combined pressures of severe drought, high crop-waste rates, and unaffordable commercial precision-agriculture platforms, which together disproportionately affect small and mid-sized growers. This paper proposes an integrated planthealth monitoring platform that combines a Raspberry-Pi field node for image capture and environmental sensing, a multimodal vision model for species identification and health assessment, and a Flutter mobile client that presents results to the grower through a simple dashboard. The client implements a layered fallback between the live Pi, an on-disk cache, and a bundled sample dataset so that it remains functional under intermittent connectivity, and it caches plant images transparently to accelerate repeated views. Two experiments evaluated the system: species identification reached 87.5 percent accuracy across four visually similar species, and time-to-first-paint ranged from 0.38 seconds on cached data to 1.34 seconds under degraded networks. The platform demonstrates that practical precision agriculture is achievable at consumer-hardware scale.

Plant phenotyping relevance

植物画像を取得し、植物の健康状態を評価する統合モニタリング基盤を開発・評価しており、画像ベースの植物状態推定が中心です。

Code and data availability

The paper describes a Raspberry Pi plant-monitoring system and two small experiments (40 houseplant photos, latency tests), but provides no public dataset, image set, code repository, or model checkpoint. No availability statements or author URLs appear in the supplied blocks, and no allowed URLs are provided.

No evidence-backed public reproduction asset is currently recorded.