Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India

Research Article

Multi-Channel Multi-Modal Concatenation-based Deep Learning Model for Leaf Infection and Soil Property Prediction

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  • @INPROCEEDINGS{10.4108/eai.23-11-2023.2343199,
        author={Padmapriya  Dhandapani},
        title={Multi-Channel Multi-Modal Concatenation-based Deep Learning Model for Leaf Infection and Soil  Property Prediction},
        proceedings={Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India},
        publisher={EAI},
        proceedings_a={IACIDS},
        year={2024},
        month={3},
        keywords={leaf infections soil properties multi-channel cnn lstm multi-modal fusion feature learning},
        doi={10.4108/eai.23-11-2023.2343199}
    }
    
  • Padmapriya Dhandapani
    Year: 2024
    Multi-Channel Multi-Modal Concatenation-based Deep Learning Model for Leaf Infection and Soil Property Prediction
    IACIDS
    EAI
    DOI: 10.4108/eai.23-11-2023.2343199
Padmapriya Dhandapani1,*
  • 1: KG College of Arts And Science, Coimbatore
*Contact email: padmapriya.d@kgcas.com

Abstract

One of the most important tasks in improving crop yield quality is accurately predicting leaf infection and its related soil properties. A Multi-channel Convolutional Neural Network (MCNN) was already designed that utilized a separate channels for learning features of soil and leaf infection images. But, it was not able to capture the spatiotemporal variance of the leaf infections and may lose data because of feature fusion at the decision stage. Hence, this article proposes a Multi-channel Multimodal Concatenation-based CNN with Long Short-Term Memory (M2C2NN-LSTM) model to improve the generalizability of feature learning for leaf infection and soil property prediction. At first, the MCNN architecture is built to learn the deep features from soil and leaf images together using DenseNets followed by the Convolutional LSTM (ConvLSTM), which helps to extract the spatiotemporal dependencies between them. During feature learning, three different types of concatenation strategies are employed to fuse the encoding of spatiotemporal features with better generalization ability and achieve robust prediction. Once the prediction process is completed, the predicted outcomes of leaf infections and related soil properties are broadcasted to the cultivators via smartphones to develop yield productivity. At last, this model is validated by the different categories of leaf infection and soil photos for cotton, pineapple, and strawberry crops plants.