Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India

Research Article

An Efficient Weed Growth Rate Estimator

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  • @INPROCEEDINGS{10.4108/eai.16-4-2022.2318148,
        author={Yash  Vishwakarma and Akhilesh A.  Waoo},
        title={An Efficient Weed Growth Rate Estimator},
        proceedings={Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India},
        publisher={EAI},
        proceedings_a={THEETAS},
        year={2022},
        month={6},
        keywords={computer vision image classification leaf counting convolutional neural network efficientnet},
        doi={10.4108/eai.16-4-2022.2318148}
    }
    
  • Yash Vishwakarma
    Akhilesh A. Waoo
    Year: 2022
    An Efficient Weed Growth Rate Estimator
    THEETAS
    EAI
    DOI: 10.4108/eai.16-4-2022.2318148
Yash Vishwakarma1,*, Akhilesh A. Waoo1
  • 1: AKS University, Satna
*Contact email: yashvishwakarma@hotmail.com

Abstract

This study drafts a new EfficientNetB0 CNN-based process of automatically classifying weed plants into different developmental phases. Images of weed plants growing within various crops across varying environmental constraints were used. About 90% of the images were used for training the proposed model. The performance of this EfficientNetB0 based convolutional neural network model was measured on a different additional set of 10% images never seen by the model. The model attained a very high 91% accuracy in identifying young singleleaf weed plants. Additionally, it attained an average accuracy of 73% in evaluating the count of leaves across all classes and an accuracy of 81% among all classes except one. The accuracy of the results conveys that this new method of using the EfficientNetB0 based model has a high potential to classify different developmental phases among distinct weed plants.