Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Classification of Medicinal Plants Using Machine Learning
    (Springer Science and Business Media Deutschland GmbH, 2022) Meshram, R.S.; Patil, N.
    Nowadays, peoples are not having information about the surrounding plants and their medicinal values. If some person wants to know about the medicinal plants, they have to contact the person who is having deep knowledge about the medicinal plants and its uses. In order to solve this problem we can use the current technology to give a tool which will help the common people to know more about the medicinal plants. For doing this we can use many machine learning techniques for classifying the medicinal plants with more accuracy. Different kind of medicinal plant species are available on the planet earth but classification of the Particular medicinal plant is very difficult without knowing about the plants first. The information about the medicinal plants is collected by the scientists and urban people. Generally this kind of knowledge is passed through generation to generation and sometimes there might be some changes in the information and its contents. So according to the current situation we can use the machine learning technology to make the tool which will be helpful to solve the medicinal plant classification problem. Machine learning model can easily classify the medicinal plants after the feature extraction and applying the model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Hybrid Deep Learning-Based Potato and Tomato Leaf Disease Classification
    (Springer Science and Business Media Deutschland GmbH, 2024) Patil, M.A.; Manur, M.; Laxuman, C.; Parane, K.; Dodamani, B.M.; Sunkad, G.
    Predicting potato and tomato leaf disease is vital to global food security and economic stability. Potatoes and tomatoes are among the most important staple crops, providing essential nutrition to millions worldwide. However, many tomato and potato leaf diseases can seriously reduce food productivity and yields. We are proposing a hybrid deep learning model that combines optimized CNN (OCNN) and optimized LSTM (OLSTM). The weight values of LSTM and CNN models are optimized using the modified raindrop optimization (MRDO) algorithm and the modified shark smell optimization (MSSO) algorithm, respectively. The OCNN model is used to extract potato leaf image features and then fed into the OLSTM model, which handles data sequences and captures temporal dependencies from the extracted features. Precision, recall, F1-score, and accuracy metrics are used to analyze the output of the proposed OCNN-OLSTM model. The experimental performance is compared without optimizing the CNN-LSTM model, individual CNN and LSTM models, and existing MobileNet and ResNet50 models. The presented model results are compared with existing available work. We have received an accuracy of 99.25% potato and 99.31% for tomato. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.