{"type":"ScholarlyArticle","title":"AI & Drone-Driven Solution to Improve Crop Yield via Precise Pest Control","authors":[{"name":"Mansour Badamassi Mamane Barkawi","slug":"mansour-badamassi-mamane-barkawi","given":"Mansour Badamassi","family":"Mamane Barkawi","affiliation":""},{"name":"Labaly Touré","slug":"labaly-toure","given":"Labaly","family":"Touré","affiliation":"Université du Sine Saloum El-Hadj Ibrahima Niass -USSEIN , Senegal"},{"name":"Ismaguil Hanadé Houmma","slug":"ismaguil-hanade-houmma","given":"Ismaguil","family":"Hanadé Houmma","affiliation":"Mohammed VI Polytechnic University Lot 660 Hay Moulay Rachid Ben Guerir , Morocco"}],"abstract":"Agriculture is vital for ensuring food security and sustaining economic growth, yet it faces substantial threats from pests and diseases, which account for up to 40% of global crop production losses annually. This study presents an innovative AI and drone-driven solution deployed by GeoMinds Africa to combat agricultural pests, specifically targeting the Thaumatotibia (Cryptophlebia) leucotreta (Meyrick) pest, which affects more than 35 kinds of plants across 37 African countries by leveraging artificial intelligence (AI) and UAV. Over 3 months, high-resolution drone imagery (DJI Mavic 3 Multispectral) was captured in Gaya City (Niger) and processed, generating a dataset of over 5,000 labeled images with the active participation of 50 farmers. After compared with other machine learning models (SVM, and Gradient Boosting), a Random Forest model was selected and trained on the labeled images, with the integrated weather data, historical records of pests, and vegetation indices such as Normalized Difference Red Edge Index (NDRE), to identify early signs of Thaumatotibia leucotreta pest infestation. As a result, the study highlights that the implementation of AI and drone technology in Gaya City improved the management of Thaumatotibia leucotreta on mango crops. The Random Forest model achieved 92% accuracy, outperforming other models. NDRE values effectively identified stress areas, with a 10% increase in detection accuracy when integrated with weather data. Field validation showed 90% farmer satisfaction, leading to a 20% reduction in crop losses and a 15% yield improvement for the 50 farmers.","doi":"10.24963/ijcai.aai4g.2024/5","url":"https://ijcai.org/proceedings_aai4g/2024/5","pdf":"https://ijcai.org/proceedings_aai4g/2024/0005.pdf","markdown":"https://ijcai.org/proceedings_aai4g/2024/5.md","bibtex":"https://ijcai.org/proceedings_aai4g/2024/bibtex/5","volume":{"slug":"aai4g-2024","title":"Proceedings of the Second IJCAI AI for Good Symposium in Africa hosted by Deep Learning Indaba","short_title":"AI for Good Africa 2024","url":"https://ijcai.org/proceedings_aai4g/2024/","year":2024,"location":"Dakar, Senegal","dates":"1-7 September 2024","editors":"Avishkar Bhoopchand, Girmaw Abebe Tadesse, Sibusisiwe Makhanya, Frank Dignum, Georgina Curto","isbn":"978-1-956792-07-2","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.aai4g.2024"},"section":"Indaba","track":"Indaba","number":5,"pages":{"first":31,"last":34},"keywords":["Indaba: Indaba"],"videos":[],"references":[{"position":1,"raw":"[EPPO, 2024] EPPO. Thaumatotibia leucotreta distribu- tion. Technical report, European and Mediterranean Plant Protection Organization, 2024. EPPO Global Database, distribution status for false codling moth (ARGPLE) across member countries; consulted country-level pres- ence/absence data as of early 2024.","title":"EPPO Global Database, distribution status for false codling moth (ARGPLE) across member countries; consulted country-level presence/absence data as of early 2024","authors":"Eppo","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[FAO, 2021] FAO. Integrated pest management: Guidelines for sustainable crop protection. Technical report, FAO Plant Production and Protection Division, Rome, Italy, 2021. FAO publication supporting sustainable agriculture and pest control strategies.","title":"FAO. Integrated pest management: Guidelines for sustainable crop protection","authors":null,"year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[John et al., 2023a] A. John, B. Sharma, and D. Gupta. Re- mote sensing and artificial intelligence for precision agri- culture: A review. Precision Agriculture, 24(2):345-367, 2023.","title":"Remote sensing and artificial intelligence for precision agriculture: A review","authors":"John","year":"2023","venue":"Precision Agriculture","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[John et al., 2023b] Matthew Abu John, Ibukunoluwa Bankole, Oluwatayo Ajayi-Moses, Tofunmi Ijila, Oluwa- timilehin Jeje, and Lalit Patil. Relevance of advanced plant disease detection techniques in disease and pest management for ensuring food security: A review. Ameri- can Journal of Plant Sciences, 14(11):1260-1295, 2023. https://doi.org/10.4236/ajps.2023.1411086.","title":"Tofunmi Ijila, Oluwatimilehin Jeje, and Lalit Patil. Relevance of advanced plant disease detection techniques in disease and pest management for ensuring food security: A review","authors":"John","year":"2023","venue":"American Journal of Plant Sciences","doi":"10.4236/ajps.2023.1411086","url":"https://doi.org/10.4236/ajps.2023.1411086","internal_url":null},{"position":5,"raw":"[Liu et al., 2022] Linyi Liu, Huiqin Ma, Anting Guo, ChaoVE Ruan, Wenjiang Huang, and Yun Geng, edi- tors. Crop Pest and Disease Remote Sensing Monitoring and Forecasting. EDP Sciences, Les Ulis, France, 2022. https://doi.org/10.1051/978-2-7598-2660-5.","title":"Crop Pest and Disease Remote Sensing Monitoring and Forecasting","authors":"Liu","year":"2022","venue":null,"doi":"10.1051/978-2-7598-2660-5","url":"https://doi.org/10.1051/978-2-7598-2660-5","internal_url":null},{"position":6,"raw":"[Mohanty et al., 2016] Sharada P. Mohanty, David P. Hughes, and Marcel Salathé. Using deep learning for image-based plant disease detec- tion. Frontiers in Plant Science, 7:1419, 2016. https://doi.org/10.3389/fpls.2016.01419.","title":"Using deep learning for image-based plant disease detection","authors":"P Sharada, David P Mohanty, Marcel Hughes, Salathé","year":"2016","venue":"Frontiers in Plant Science","doi":"10.3389/fpls.2016.01419","url":"https://doi.org/10.3389/fpls.2016.01419","internal_url":null},{"position":7,"raw":"[Rajagopal and MS, 2023] Manoj Kumar Rajagopal and Bala Murugan MS. Artificial intelligence based drone for early disease detection and precision pesti- cide management in cashew farming. arXiv preprint arXiv:2303.08556, 2023. https://doi.org/10.48550/arXiv. 2303.08556.","title":"Artificial intelligence based drone for early disease detection and precision pesticide management in cashew farming","authors":"Manoj Kumar, Rajagopal, Bala Murugan, M S","year":"2023","venue":null,"doi":"10.48550/arxiv.2303.08556","url":"https://doi.org/10.48550/arXiv.2303.08556","internal_url":null},{"position":8,"raw":"[Sougué, 2016] Edmond Sougué. Malanville-gaya, une dynamique de territorialisation à la frontière bénin- niger. Territoire en mouvement Revue de géographie et aménagement, (29), 2016. https://doi.org/10.4000/tem. 3216.","title":"Edmond Sougué. Malanville-gaya, une dynamique de territorialisation à la frontière béninniger","authors":"Sougué","year":"2016","venue":"Territoire en mouvement Revue de géographie et aménagement","doi":"10.4000/tem.3216","url":"https://doi.org/10.4000/tem.3216","internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}