AgroVision- Platform Independent Crop Analysis Using Deep Learning Techniques

Abstract

Despite numerous efforts to incorporate emerging innovations into the agricultural domain for increasing crop yield and actively managing the state of the fields, it remains difficult for the industry to implement cutting-edge technologies in practice. This paper proposes AgroVision – a web-based intelligent multimodal system for comprehensive analysis of crops around the world. Designed using a three-layer scalable architecture, the system includes four modules – CNN-based growth stages and plant diseases recognition, AI Chatbot with LLM capabilities and RAG support, as well as the video analysis tool for detecting plant density and weeds. The key technology behind the core image analysis functionality of AgroVision is represented by the efficient Vision Mamba (ViM) architecture, which allows for analysing multiple tasks simultaneously using only one image uploaded by the user. Based on the extensive dataset called "New Plant Diseases Dataset" containing over 87 thousand images divided into 38 classes, the ViM model demonstrates exceptional results achieving weighted average F1-Score of 97.1%. Considering that the inference latency of the model does not exceed 25-40 milliseconds, the system can be deployed at the edge, providing an easy-to-use solution for farmers.

Plant phenotyping relevance

植物の成長段階、病害、植物密度を画像から推定するウェブ型解析プラットフォームを提案しており、表現型取得・推定が研究の中心である。

Code and data availability

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