SMART AGRICULTURE: ENHANCING PLANT DISEASE DETECTION WITH DRONE TECHNOLOGY
DOI:
https://doi.org/10.63458/ijerst.v3i3.117Keywords:
GPS Navigation, Smart Irrigation, Drone,Geo Referenced Imaging, Leaf Disease Detection, Wireless Communication.Abstract
This technology transforms farm management by optimizing resource consumption and improving crop health through the integration of AI-driven disease diagnosis and targeted watering. A quadcopter drone equipped with a GPS and high-resolution camera captures geo-referenced images for aerial surveys, which are processed in real-time by an onboard edge intelligence unit. Advanced AI technologies locate and map affected areas, instantly linking diseases to precise locations. Only the affected areas may receive precise water and nutrient supplies if this data is wirelessly transmitted to intelligent irrigation systems. The technique reduces unnecessary fertilizer and pesticide use, eliminates water waste, and boosts yields while promoting sustainability. Early disease diagnosis helps prevent large-scale epidemics and supports proactive management. This method combines drone-based remote sensing, AI-powered analysis, and precision irrigation to boost agricultural yield while conserving resources.
References
Shahi, T.B.; Xu, C.-Y.; Neupane, A.; Guo, W. Recent, “Advances in Crop Disease Detection Using UAV and Deep Learning Techniques”. Remote Sens. 15, 2450., 2023. DOI: https://doi.org/10.3390/rs15092450
Yamamoto, S.; Nomoto, S.; Hashimoto, N.; Maki, M.; Hongo, C.; Shiraiwa, T. , “Monitoring spatial and time-series variations in red crown rot damage of soybean in farmer fields based on UAV remote sensing.” Plant Prod. Sci. 26, pp.36–47. 2023. DOI: https://doi.org/10.1080/1343943X.2023.2178469
Md. Jobayer Rahman, Md. Shakil Ahmed, Swapnil Biswas, Anika Tabassum Orchi, Raiyan Rahman, and A.K.M. Muzahidul Islam , “CropCare: Advanced Crop Management System with Intelligent Advisory and Machine Learning Techniques”. 2024. DOI: https://doi.org/10.1109/ICEEICT62016.2024.10534582
Muhammad Suleman Memon, Pardeep Kumar, Azeem Ayaz Mirani, “Deep Learning and IoT: The Enabling Technologies Towards Smart Farming”., pp.47-60. 2020. DOI: https://doi.org/10.4018/978-1-7998-2803-7.ch003
Ruben Chin, Cagatay Catal & Ayalew Kassahun , “Plant disease detection using drones in precision agriculture Revie”, Open accessPublished: Vol. 24, pp.1663–1682, 2023. DOI: https://doi.org/10.1007/s11119-023-10014-y
El Mehdi Raouhi, Mohamed Lachgar, Hamid Hrimech, Ali Kartit, LTI Laboratory, ENSA, El Jadida, Morocco1,2,4 LAMSAD Laboratory, ENSA, University Settat, Berrechid, Morocco3 Unmanned Aerial Vehicle-based Applications in Smart Farming., Vol.14, No.6.2023 DOI: https://doi.org/10.14569/IJACSA.2023.01406123
Gregorio Z. Gamboa Jr., Analyn S. Morite, Robert R. Bacarro, Rowena A. Plando, VrianJayYlaya, elieson john serna, “Rice field health monitoring system using a drone with ai interface”.,Sci. Int.(Lahore),35(3),181-184. 2023.
Mohamed Emimi , Mohamed Khaleel , Abobakr Alkrash, “The Current Opportunities and Challenges in Drone Technology”, ISSN:2959-9229, pp.74-89. 2023.
Zhihong Zhang et al., “Precision Variable-rate Control System for MiniUAV-based Pesticide Application”, To cite this article: J. Phys.: Conf. Ser. 2557 012006. 2023. DOI: https://doi.org/10.1088/1742-6596/2557/1/012006
Wei Zhao, Meini Wan, and V. T. Pham. , “Unmanned Aerial Vehicle and Geospatial Analysis in Smart Irrigation and Crop Monitoring on IoT Platform”, 1/4213645. 2023. DOI: https://doi.org/10.1155/2023/4213645
Zhang, T.; Xu, Z.; Su, J.; Yang, Z.; Liu, C.; Chen, W.-H.; Li, J., “Ir-unet: Irregular segmentation u-shape network for wheat yellow rust detection by UAV multispectral imagery”. Remote Sens., 13, 3892. 2021. DOI: https://doi.org/10.3390/rs13193892
Hu, G.; Zhu, Y.; Wan, M.; Bao, W.; Zhang, Y.; Liang, D.; Yin, C. “Detection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks”. Geocarto Int., 37, 3520–3539. 2023. DOI: https://doi.org/10.1080/10106049.2020.1864025
Sumathadas, Arindam gosh, sarit pal, “Internet-of-Things-Enabled Precision Agriculture for Sustainable Rural Development, Classification: LCC”, S494.5.P73 P74 2024 | DDC 630.285—dc23. 2024.
Ravi Ray Chaudhary, Kalyan Devappa, Himanshi Agrawal P. Malathi, Aarti S. Gaikwad, Abhijit Janardan Patankar , “A critical analysis of crop management using Machine Learning towards smart and precise farming.” 2023 DOI: https://doi.org/10.62110/sciencein.jist.2024.v12.809
Agarwal, M., Singh, A., Arjaria, S., Sinha, A., and Gupta, S. “ToLeD: tomato leaf disease detection using convolution neural network.” Proc. Comput. Sci. 167, 293–301. 2022. DOI: https://doi.org/10.1016/j.procs.2020.03.225
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