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Autonomous System for Locating the Maize Plant Infected by Fall Armyworm

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Artificial Intelligence Application in Networks and Systems (CSOC 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 724))

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Abstract

Digital agriculture helps farmers collect, analyze, and monitor farms while in remote locations, thus spending less time and money while getting more yield. Digital agriculture employs a variety of techniques, including image processing and machine learning. Previous studies have proposed various systems that use unmanned aerial vehicles (UAV) and machine learning techniques to precisely predict maize plants infested by various diseases. In this paper, we propose an extension of the system to locate the exact position of infected maize plants, as well as estimate the size of the infected area. This enables farmers to take appropriate action based on the precise location of the infection, rather than the entire farm. The proposed system performs three tasks: first, it crops UAV images into smaller images and transplants the Global Positioning System coordinates (GPSc) from UAV images into cropped images; second, it extracts the coordinates of the infested maize plant and counts similar GPSc that determine the size of the infected area on every UAV image, and finally, it sends a report to the farmer indicating the infested plants and the size covered by infection on every UAV image. The report helps farmers act quickly and only spray pesticides on infected areas, which saves them time and money. Using a dataset from a maize farm infected by fall armyworm, we show that the system is effective in locating the infected plants and areas.

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References

  1. Singh, P., et al.: Hyperspectral remote sensing in precision agriculture: Present status, challenges, and future trends. LTD (2020)

    Google Scholar 

  2. Bhoi, S.K., et al.: An Internet of Things assisted Unmanned Aerial Vehicle based artificial intelligence model for rice pest detection. Microprocess. Microsyst. 80, 103607 (2021)

    Google Scholar 

  3. Gomez Selvaraj, M., et al.: Detection of banana plants and their major diseases through aerial images and machine learning methods: a case study in DR Congo and Republic of Benin. ISPRS J. Photogramm. Remote Sens. 169, 110–124 (2020)

    Google Scholar 

  4. Wu, B., et al.: Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning. For. Ecol. Manage. 486, 118986 (2021)

    Google Scholar 

  5. Yadav, S., Sengar, N., Singh, A., Singh, A., Dutta, M.K.: Identification of disease using deep learning and evaluation of bacteriosis in peach leaf. Ecol. Inform. 61, 101247 (2021)

    Google Scholar 

  6. Tsouros, D.C., Bibi, S., Sarigiannidis, P.G.: A review on UAV-based applications for precision agriculture. Information 10(11), 349 (2019). https://doi.org/10.3390/info10110349

    Article  Google Scholar 

  7. Ishengoma, F.S., Rai, I.A., Ngoga, S.R.: Hybrid convolution neural network model for a quicker detection of infested maize plants with fall armyworms using UAV-based images. Ecol. Inform. 67, 101502 (2022)

    Google Scholar 

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Funding

The African Center of Excellence on the Internet of Things funded this research (ACEIoT).

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Correspondence to Farian S. Ishengoma .

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Ishengoma, F.S., Rai, I.A., Gatare, I. (2023). Autonomous System for Locating the Maize Plant Infected by Fall Armyworm. In: Silhavy, R., Silhavy, P. (eds) Artificial Intelligence Application in Networks and Systems. CSOC 2023. Lecture Notes in Networks and Systems, vol 724. Springer, Cham. https://doi.org/10.1007/978-3-031-35314-7_10

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