Automated Detection of Pineapple Plants in Drone-Captured Aerial Imagery for Precision Agriculture
Resumo
Pineapple harvesting remains largely manual due to scattered planting patterns and complex fruit structure. This study presents a method for detecting pineapple plants in large orthomosaic images using the Slicing Aided Hyper Inference (SAHI) technique combined with the YOLOv8 segmentation model. SAHI divides large images into smaller patches, enabling accurate detection. A dataset of 867 training and 97 validation images from two orthomosaics was used, with the model achieving 93% precision and 88% accuracy. Despite high precision, chal lenges with false negatives suggest future improvements. This approach shows promise for automating pineapple harvesting and improving agricultural efficiency.
Palavras-chave:
Pineapple detection, deep learning, YOLOv8, SAHI, agricultural automation, orthomosaic images, precision agriculture
Referências
U.S. Department of Agriculture (USDA), Pineapple Production and Export Data, 2023.
ABAPA - Associação Brasileira dos Produtores de Abacaxi, Relatório de Exportação de Abacaxi 2023, 2023.
FAO - Food and Agriculture Organization. World Programme for the Census of Agriculture. 2023.
L. M. M. Alves, Uma análise da competitividade das exportações de fruticultura cearense e brasileira: O caso do abacaxi e da melancia, Dissertação (Mestrado) - Universidade Federal do Ceará, Centro de Ciências Agrárias, Fortaleza-CE, 2009.
P. E. Wiranthi and F. Mubarok, Competitiveness and the Factors Affecting Export of the Indonesia Canned Pineapple in the World and the Destination Countries, KnE Life Sciences, vol. 2, no. 6, pp. 339-352, 2017. DOI: 10.18502/kls.v2i6.1056.
L. Kleemann, A. Abdulai, and M. Buss, Certification and Access to Export Markets: Adoption and Return on Investment of Organic-Certified Pineapple Farming in Ghana, World Development, vol. 64, pp. 79-92, 2014. DOI: 10.1016/j.worlddev.2014.05.005.
A. K. Sharma, H. H. C. Nguyen, T. X. Bui, S. Bhardwa, and D. V. Thang, An Approach to Ripening of Pineapple Fruit with Model Yolo v5, in 2022 IEEE 7th International Conference for Convergence in Technology (I2CT), 2022, pp. 1-5. DOI: 10.1109/I2CT54291.2022.9824067.
Z. Li, Y.-W. Chong, M. N. Ab Wahab, G.-K. Lim, and R. Dawood, Classification and Prediction of Pineapple Quality using Deep Learning, in 2023 4th International Conference on Big Data Analytics and Practices (IBDAP), 2023, pp. 1-6. DOI: 10.1109/IBDAP58581.2023.10271948.
Y. Lai, R. Ma, Y. Chen, T. Wan, R. Jiao, and H. He, A Pineapple Target Detection Method in a Field Environment Based on Improved YOLOv7, Applied Sciences, vol. 13, no. 4, Article 2691, 2023. [Online]. Available: [link]. DOI: 10.3390/app13042691.
R.Wan Nurazwin Syazwani, H. Muhammad Asraf, M.A. Megat Syahirul Amin, and K.A. Nur Dalila, Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning, Alexandria Engineering Journal, vol. 61, no. 2, pp. 1265-1276, 2022. DOI: 10.1016/j.aej.2021.06.053.
N. P. T. Anh, S. Hoang, D. Van Tai, and B. L. C. Quoc, Developing Robotic System for Harvesting Pineapples, in 2020 International Conference on Advanced Mechatronic Systems (ICAMechS), 2020, pp. 39-44. DOI: 10.1109/ICAMechS49982.2020.9310079.
R. Rahutomo, A. S. Perbangsa, Y. Lie, T. W. Cenggoro, and B. Pardamean, Artificial Intelligence Model Implementation in Web-Based Application for Pineapple Object Counting, in 2019 International Conference on Information Management and Technology (ICIMTech), vol. 1, pp. 525-530. DOI: 10.1109/ICIMTech.2019.8843741.
J. Hobbs, P. Prakash, R. Paull, H. Hovhannisyan, B. Markowicz, and G. Rose, Large-Scale Counting and Localization of Pineapple Inflorescence Through Deep Density-Estimation, Frontiers in Plant Science, vol. 11, Article 599705, Jan. 2021. DOI: 10.3389/fpls.2020.599705.
Akyon, F. C., Altinuc, S. O., Temizel, A., Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection, 2022 IEEE International Conference on Image Processing (ICIP), 2022, pp. 966-970, DOI: 10.1109/ICIP46576.2022.9897990.
G. Jocher, A. Chaurasia, and J. Qiu, Ultralytics YOLO, version 8.0.0, Jan. 2023. [Online]. Available: [link]. [Accessed: Sep. 08, 2024].
Z. Wan, H. Wang, H. Li, L. Zhao, and Z. Ma, Pineapples Detection and Segmentation Based on Faster and Mask R-CNN in UAV Imagery, Remote Sensing, vol. 15, no. 3, pp. 739-755, 2023. DOI: 10.3390/rs15030739.
ABAPA - Associação Brasileira dos Produtores de Abacaxi, Relatório de Exportação de Abacaxi 2023, 2023.
FAO - Food and Agriculture Organization. World Programme for the Census of Agriculture. 2023.
L. M. M. Alves, Uma análise da competitividade das exportações de fruticultura cearense e brasileira: O caso do abacaxi e da melancia, Dissertação (Mestrado) - Universidade Federal do Ceará, Centro de Ciências Agrárias, Fortaleza-CE, 2009.
P. E. Wiranthi and F. Mubarok, Competitiveness and the Factors Affecting Export of the Indonesia Canned Pineapple in the World and the Destination Countries, KnE Life Sciences, vol. 2, no. 6, pp. 339-352, 2017. DOI: 10.18502/kls.v2i6.1056.
L. Kleemann, A. Abdulai, and M. Buss, Certification and Access to Export Markets: Adoption and Return on Investment of Organic-Certified Pineapple Farming in Ghana, World Development, vol. 64, pp. 79-92, 2014. DOI: 10.1016/j.worlddev.2014.05.005.
A. K. Sharma, H. H. C. Nguyen, T. X. Bui, S. Bhardwa, and D. V. Thang, An Approach to Ripening of Pineapple Fruit with Model Yolo v5, in 2022 IEEE 7th International Conference for Convergence in Technology (I2CT), 2022, pp. 1-5. DOI: 10.1109/I2CT54291.2022.9824067.
Z. Li, Y.-W. Chong, M. N. Ab Wahab, G.-K. Lim, and R. Dawood, Classification and Prediction of Pineapple Quality using Deep Learning, in 2023 4th International Conference on Big Data Analytics and Practices (IBDAP), 2023, pp. 1-6. DOI: 10.1109/IBDAP58581.2023.10271948.
Y. Lai, R. Ma, Y. Chen, T. Wan, R. Jiao, and H. He, A Pineapple Target Detection Method in a Field Environment Based on Improved YOLOv7, Applied Sciences, vol. 13, no. 4, Article 2691, 2023. [Online]. Available: [link]. DOI: 10.3390/app13042691.
R.Wan Nurazwin Syazwani, H. Muhammad Asraf, M.A. Megat Syahirul Amin, and K.A. Nur Dalila, Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning, Alexandria Engineering Journal, vol. 61, no. 2, pp. 1265-1276, 2022. DOI: 10.1016/j.aej.2021.06.053.
N. P. T. Anh, S. Hoang, D. Van Tai, and B. L. C. Quoc, Developing Robotic System for Harvesting Pineapples, in 2020 International Conference on Advanced Mechatronic Systems (ICAMechS), 2020, pp. 39-44. DOI: 10.1109/ICAMechS49982.2020.9310079.
R. Rahutomo, A. S. Perbangsa, Y. Lie, T. W. Cenggoro, and B. Pardamean, Artificial Intelligence Model Implementation in Web-Based Application for Pineapple Object Counting, in 2019 International Conference on Information Management and Technology (ICIMTech), vol. 1, pp. 525-530. DOI: 10.1109/ICIMTech.2019.8843741.
J. Hobbs, P. Prakash, R. Paull, H. Hovhannisyan, B. Markowicz, and G. Rose, Large-Scale Counting and Localization of Pineapple Inflorescence Through Deep Density-Estimation, Frontiers in Plant Science, vol. 11, Article 599705, Jan. 2021. DOI: 10.3389/fpls.2020.599705.
Akyon, F. C., Altinuc, S. O., Temizel, A., Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection, 2022 IEEE International Conference on Image Processing (ICIP), 2022, pp. 966-970, DOI: 10.1109/ICIP46576.2022.9897990.
G. Jocher, A. Chaurasia, and J. Qiu, Ultralytics YOLO, version 8.0.0, Jan. 2023. [Online]. Available: [link]. [Accessed: Sep. 08, 2024].
Z. Wan, H. Wang, H. Li, L. Zhao, and Z. Ma, Pineapples Detection and Segmentation Based on Faster and Mask R-CNN in UAV Imagery, Remote Sensing, vol. 15, no. 3, pp. 739-755, 2023. DOI: 10.3390/rs15030739.
Publicado
06/11/2024
Como Citar
LIMA, Matheus Arroyo de; MARTINS, Thiago Mantovani; ALMEIDA, Vitor Matheus Soares Siqueira De; JARDIM, Rafael Buosi; ZANI, Victor Hugo; BARBOSA, Luiza Hoehl Loureiro Alves.
Automated Detection of Pineapple Plants in Drone-Captured Aerial Imagery for Precision Agriculture. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 19. , 2024, Rio Paranaíba/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 42-47.
DOI: https://doi.org/10.5753/wvc.2024.34011.
