Deep Convolutional Neural Network Architecture for Plant Seedling Classification


  • N. C. Kundur Department of Computer Science and Engineering, JSS Academy of Technical Education, India
  • P. B. Mallikarjuna Department of Computer Science and Engineering, JSS Academy of Technical Education, India
Volume: 12 | Issue: 6 | Pages: 9464-9470 | December 2022 |


Weed control is essential in agriculture since weeds reduce yields, increase production cost, impede harvesting, and degrade product quality. As a result, it is indeed critical to recognize weeds early in their vegetation cycle to evade negative impacts to crop growth. Earlier traditional methods used machine learning to determine crops along with weed species, but they had issues with weed detection efficiency at early growth stages. The current work proposes the implementation of a deep learning method that provides accurate results for precise weed recognition. Two different deep convolution neural networks have been used for our classification framework, namely Efficient Net B2 and Efficient Net B4. The plant seedlings dataset is utilized to investigate the proposed work. The evaluation metrics average accuracy, precision, recall, and F1-score were used. The findings demonstrate that the proposed approach is capable of differentiating between 12 species of a plant seedling dataset which contains 3 crops and 9 weeds. The average classification accuracy and F1 score are 99.00% for our Efficient Net B4 model and 97.00% for the Efficient Net B2. In addition, the proposed Efficient Net-B4 model performance is compared to the one of existing models on the plant seedlings dataset and the results showed that the proposed model Efficient Net B4 has superior performance. We intend to detect diseases in the identified plant species in our future research.


deep learning, efficient net, machine learning, weed recognition, plant seedling classification


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How to Cite

N. C. Kundur and P. B. Mallikarjuna, “Deep Convolutional Neural Network Architecture for Plant Seedling Classification”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 6, pp. 9464–9470, Dec. 2022.


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