Coffee Plant Leaf Disease Detection for Digital Agriculture

Abstract

In an effort to advance Digital Agriculture, this paper provides a comparative assessment of Artificial Neural Networks for intelligent detection of a major biotic stress factors in coffee cultivation. Through a multi-class Computer Vision task, the superior performance of Convolutional Neural Networks, notably the ShuffleNet architecture, was discerned, further substantiated by statistical analyses. This model's performance, akin to state-of-the-art solutions, was achieved with reduced training data and parameter requirements. Robustness was affirmed through external validation using alternative datasets. This contribution directly enhances coffee plantations' quality and supports the development of Edge Computing devices for Agricultural IoT.

Plant phenotyping relevance

コーヒー葉の病害状態を画像から検出するコンピュータビジョン手法を比較・評価し、外部データセットで検証しているため、植物病害フェノタイピング手法が中心です。

Code and data availability

The paper's Declarations section explicitly states that the datasets generated and/or analysed during the study are available at the authors' public GitHub repository (https://github.com/elloa/jis-2023), which is an allowed URL. This repository is the paper-specific asset covering the coffee leaf disease image datasets