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ANN-Based Continual Classification in Agriculture

Author

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  • Yang Li

    (College of Mechanical and Electrical Engineering, Shihezi University, Xinjiang 832003, China
    School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Xuewei Chao

    (College of Mechanical and Electrical Engineering, Shihezi University, Xinjiang 832003, China)

Abstract

In the area of plant protection and precision farming, timely detection and classification of plant diseases and crop pests play crucial roles in the management and decision-making. Recently, there have been many artificial neural network (ANN) methods used in agricultural classification tasks, which are task specific and require big datasets. These two characteristics are quite different from how humans learn intelligently. Undoubtedly, it would be exciting if the models can accumulate knowledge to handle continual tasks. Towards this goal, we propose an ANN-based continual classification method via memory storage and retrieval, with two clear advantages: Few data and high flexibility. This proposed ANN-based model combines a convolutional neural network (CNN) and generative adversarial network (GAN). Through learning of the similarity between input paired data, the CNN part only requires few raw data to achieve a good performance, suitable for a classification task. The GAN part is used to extract important information from old tasks and generate abstracted images as memory for the future task. Experimental results show that the regular CNN model performs poorly on the continual tasks (pest and plant classification), due to the forgetting problem. However, our proposed method can distinguish all the categories from new and old tasks with good performance, owing to its ability of accumulating knowledge and alleviating forgetting. There are so many possible applications of this proposed approach in the agricultural field, for instance, the intelligent fruit picking robots, which can recognize and pick different kinds of fruits; the plant protection is achieved by automatic identification of diseases and pests, which can continuously improve the detection range. Thus, this work also provides a reference for other studies towards more intelligent and flexible applications in agriculture.

Suggested Citation

  • Yang Li & Xuewei Chao, 2020. "ANN-Based Continual Classification in Agriculture," Agriculture, MDPI, vol. 10(5), pages 1-15, May.
  • Handle: RePEc:gam:jagris:v:10:y:2020:i:5:p:178-:d:359442
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    References listed on IDEAS

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    1. Jaime Grutzendler & Narayanan Kasthuri & Wen-Biao Gan, 2002. "Long-term dendritic spine stability in the adult cortex," Nature, Nature, vol. 420(6917), pages 812-816, December.
    2. Andrzej Przybylak & Radosław Kozłowski & Ewa Osuch & Andrzej Osuch & Piotr Rybacki & Przemysław Przygodziński, 2020. "Quality Evaluation of Potato Tubers Using Neural Image Analysis Method," Agriculture, MDPI, vol. 10(4), pages 1-11, April.
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    Cited by:

    1. Alper Taner & Yeşim Benal Öztekin & Hüseyin Duran, 2021. "Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut," Sustainability, MDPI, vol. 13(12), pages 1-13, June.
    2. Tan Wang & Xianbao Xu & Cong Wang & Zhen Li & Daoliang Li, 2021. "From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production," Agriculture, MDPI, vol. 11(2), pages 1-26, February.
    3. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    4. Awe, Olushina Olawale & Dias, Ronaldo, 2022. "Comparative Analysis of ARIMA and Artificial Neural Network Techniques for Forecasting Non-Stationary Agricultural Output Time Series," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 14(4), December.
    5. Mohammad Amin Amani & Francesco Marinello, 2022. "A Deep Learning-Based Model to Reduce Costs and Increase Productivity in the Case of Small Datasets: A Case Study in Cotton Cultivation," Agriculture, MDPI, vol. 12(2), pages 1-13, February.
    6. Claudia C. Tusell-Rey & Oscar Camacho-Nieto & Cornelio Yáñez-Márquez & Yenny Villuendas-Rey & Ricardo Tejeida-Padilla & Carmen F. Rey Benguría, 2022. "A Priori Determining the Performance of the Customized Naïve Associative Classifier for Business Data Classification Based on Data Complexity Measures," Mathematics, MDPI, vol. 10(15), pages 1-19, August.

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