Recognizing Plant species using Digitized leaves- A comparative study

Patil, Sumedh; Patra, Baba; Goyal, Neha; Gupta, Kapil

doi:10.1109/icoei51242.2021.9453003

Cited by 4 publications

(1 citation statement)

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“…Zhang et al [1] briefly introduced the existing plant species identification methods and summarized each method's advantages and disadvantages. Patil et al [28] performed a comparative analysis of various methods for plant identification, and experiments conducted in Swedish leaves verified the effectiveness of machine learning and CNN-based classification models. Wäldchen et al [29] synthesized and compared the preliminary studies of computer vision methods in plant species identification.…”

Section: Introductionmentioning

confidence: 99%

Research on Plant Species Identification Based on Improved Convolutional Neural Network

Yuan¹,

Liu²,

Song³

et al. 2023

Phyton

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Plant species recognition is an important research area in image recognition in recent years. However, the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy. Therefore, ShuffleNetV2 was improved by combining the current hot concern mechanism, convolution kernel size adjustment, convolution tailoring, and CSP technology to improve the accuracy and reduce the amount of computation in this study. Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning. The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum. In this paper, a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed, containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University. Finally, the improved model is compared with the baseline version of the model, which achieves better results in terms of improving accuracy and reducing the computational effort. The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%, and the recognition precision reaches up to 93.6%, which is 5.1% better than the original model and reduces the computational effort by about 31% compared with the original model. In addition, the experimental results were evaluated using metrics such as the confusion matrix, which can meet the requirements of professionals for the accurate identification of plant species.

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Section: Introductionmentioning

confidence: 99%