PlanteSaine: An Artificial Intelligent empowered mobile application for pests and disease management for maize, tomato, and onion farmers in Burkina Faso

Abstract

This study presents PlanteSaine, a novel mobile application powered by Artificial Intelligence (AI) models explicitly designed for maize, tomato, and onion farmers in Burkina Faso. Agriculture in Burkina Faso, like many developing nations, faces substantial challenges from plant pests and diseases, posing threats to both food security and economic stability. PlanteSaine addresses these challenges by offering a comprehensive solution that provides farmers with real-time identification of pests and diseases. Farmers capture images of affected plants with their smartphones, and PlanteSaine's AI system analyzes these images to provide accurate diagnoses. The application's offline functionality ensures accessibility even in remote areas with limited internet connectivity, while its messaging feature facilitates communication with agricultural authorities for guidance and support. Additionally, PlanteSaine includes an emergency alert mechanism to notify farmers about pest and disease outbreaks, enhancing their preparedness to deal with these threats. An AI-driven framework, featuring an image feature extraction phase with EfficientNetB3 and an artificial neural network (ANN) classifier, was developed and integrated into PlanteSaine. The evaluation of PlanteSaine demonstrates its superior performance compared to baseline models, showcasing its effectiveness in accurately detecting diseases and pests across maize, tomato, and onion crops. Overall, this study highlights the potential of PlanteSaine to revolutionize agricultural technology in Burkina Faso and beyond. Leveraging AI and mobile computing, PlanteSaine provides farmers with accessible and reliable pest and disease management tools, ultimately contributing to sustainable farming practices and enhancing food security. The success of PlanteSaine underscores the importance of interdisciplinary approaches in addressing pressing challenges in global agriculture

Plant phenotyping relevance

画像から作物の病害状態を推定するAI手法とアプリを開発・評価しており、植物病害の観測・分類が中心的な技術貢献である。害虫管理機能も含むが、植物画像に基づく病害診断は対象範囲に該当する。

Code and data availability

The paper describes a paper-specific image dataset (~29,000 field images of maize, tomato, and onion pests/diseases collected via Kobotoolbox) and trained EfficientNetB3-based classifiers. The Data Availability Statement points to www.ppedmas.org and the Google Play Store, but neither URL appears in the allowed_urls,so

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