Abstract
This work introduces a novel approach to remotely count and monitor potato plants in high-altitude regions of India using an unmanned aerial vehicle (UAV) and an artificial intelligence (AI)-based deep learning (DL) network. The proposed methodology involves the use of a self-created AI model called PlantSegNet, which is based on VGG-16 and U-Net architectures, to analyze aerial RGB images captured by a UAV. To evaluate the proposed approach, a self-created dataset of aerial images from different planting blocks is used to train and test the PlantSegNet model. The experimental results demonstrate the effectiveness and validity of the proposed method in challenging environmental conditions. The proposed approach achieves pixel accuracy of 98.65%, a loss of 0.004, an Intersection over Union (IoU) of 0.95, and an F1-Score of 0.94. Comparing the proposed model with existing models, such as Mask-RCNN and U-NET, demonstrates that PlantSegNet outperforms both models in terms of performance parameters. The proposed methodology provides a reliable solution for remote crop counting in challenging terrain, which can be beneficial for farmers in the Himalayan regions of India. The methods and results presented in this paper offer a promising foundation for the development of advanced decision support systems for planning planting operations.
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The data generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. The code will also be given on reasonable request. Moreover, data will be uploaded in GitHub after the publication of this article.
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DT played a key role in the development and implementation of the AI-based Deep Learning network, PlantSegNet. He was responsible for conceptualizing the network architecture, drawing inspiration from VGG-16 and U-Net models, and adapting them for plant counting in aerial images. He curated and organized the self-created dataset of aerial images from different planting blocks, ensuring it was diverse and representative of the challenging environmental conditions in high-altitude regions. Divyansh Thakur conducted extensive experiments to train and test the PlantSegNet model using the dataset, and he analyzed the results to validate the proposed methodology’s effectiveness and reliability. He was involved in comparing PlantSegNet with other existing models, such as Mask-RCNN and U-Net, to demonstrate its superiority in terms of performance parameters. Divyansh Thakur contributed to writing and revising sections of the paper related to the AI model, the experimental setup, and the evaluation of results.
SS contributed to the selection and setup of the UAV and remote sensing equipment used to capture the aerial RGB images for analysis. He assisted in the field experiments, overseeing the data collection process and managing any technical challenges encountered during the UAV flights and image capture. Srikant Srinivasan actively participated in the discussions related to the paper's methodology, results interpretation, and conclusions drawn from the study.
All authors contributed to the critical review of the manuscript and approved the final version for submission.
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Thakur, D., Srinivasan, S. AI-PUCMDL: artificial intelligence assisted plant counting through unmanned aerial vehicles in India’s mountainous regions. Environ Monit Assess 196, 406 (2024). https://doi.org/10.1007/s10661-024-12550-0
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DOI: https://doi.org/10.1007/s10661-024-12550-0