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PlanteSaine: An Artificial Intelligent Empowered Mobile Application for Pests and Disease Management for Maize, Tomato, and Onion Farmers in Burkina Faso

Author

Listed:
  • Obed Appiah

    (Competence Centre, West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Blvd Mouammar Kadafi, Patte d’oie, Ouagadougou 06 BP 9507, Burkina Faso
    Department of Computer Science and Informatics, University of Energy and Natural Resources (UENR), Sunyani P.O. Box 214, Ghana)

  • Kwame Oppong Hackman

    (Competence Centre, West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Blvd Mouammar Kadafi, Patte d’oie, Ouagadougou 06 BP 9507, Burkina Faso)

  • Belko Abdoul Aziz Diallo

    (Competence Centre, West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Blvd Mouammar Kadafi, Patte d’oie, Ouagadougou 06 BP 9507, Burkina Faso)

  • Kehinde O. Ogunjobi

    (Competence Centre, West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Blvd Mouammar Kadafi, Patte d’oie, Ouagadougou 06 BP 9507, Burkina Faso)

  • Son Diakalia

    (Directorate of Plant Protection and Packaging (DPVC), Ministry of Agriculture, Animal Resources and Fisheries (MARAH), Ouagadougou 03 BP 7005, Burkina Faso
    Gaoua University Center, Nazi BONI University, Bobo-Dioulasso 01 BP 1091, Burkina Faso)

  • Ouedraogo Valentin

    (Competence Centre, West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Blvd Mouammar Kadafi, Patte d’oie, Ouagadougou 06 BP 9507, Burkina Faso)

  • Damoue Abdoul-Karim

    (Afrique Géosciences, S/C 11 BP 178 Ouaga CMS 11, Ouagadougou, Burkina Faso)

  • Gaston Dabire

    (Gaoua University Center, Nazi BONI University, Bobo-Dioulasso 01 BP 1091, 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

Suggested Citation

  • Obed Appiah & Kwame Oppong Hackman & Belko Abdoul Aziz Diallo & Kehinde O. Ogunjobi & Son Diakalia & Ouedraogo Valentin & Damoue Abdoul-Karim & Gaston Dabire, 2024. "PlanteSaine: An Artificial Intelligent Empowered Mobile Application for Pests and Disease Management for Maize, Tomato, and Onion Farmers in Burkina Faso," Agriculture, MDPI, vol. 14(8), pages 1-23, July.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:8:p:1252-:d:1445820
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    References listed on IDEAS

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    1. Zahid Ullah & Najah Alsubaie & Mona Jamjoom & Samah H. Alajmani & Farrukh Saleem, 2023. "EffiMob-Net: A Deep Learning-Based Hybrid Model for Detection and Identification of Tomato Diseases Using Leaf Images," Agriculture, MDPI, vol. 13(3), pages 1-13, March.
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