Department of Computer Science and Information Systems

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Author: search.filters.author.Raman, SundaresanSubject: search.filters.subject.Neural networks

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    Analysis of Transfer and Residual Learning for Detecting Plant Diseases Using Images of Leaves
    (Springer, 2018-09) Raman, Sundaresan
    The study of plant diseases is critical for alleviating the problem of food security all over the world. The most critical step in mitigating this problem is the correct and appropriate timely identification of the disease. The first step in identification of a disease is visual inspection. The massive scale of this problem and lack of professionals create a need for a automated accurate visual inspection technique. Recent advances in the field of computer vision, primarily through techniques such as use of convolutional neural networks and deep learning have generated impressive results in the field of image classification and object recognition. In this paper, we address the problem of detecting plant diseases using images of leaves using different state-of-the-art approaches. We use the Plant Village dataset comprising of 86,198 images of 25 crops across 57 classes (healthy and specific diseases). The images are of high quality and have been taken manually under appropriate lighting conditions. On this dataset, our model is able to attain a significantly high average accuracy of 99.374% using transfer learning on state-of-the-art models trained on the ILSVRC 2012 dataset having 1.2 million images across 1000 classes
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    LWCNN: a lightweight convolutional neural network for agricultural crop protection
    (ACM Digital Library, 2022-07) Raman, Sundaresan; Chamola, Vinay
    Automatic identification of plant diseases is critical for agricultural crop protection so as to enhance the crop yield. The recent advances in deep learning and image processing gives hope for the development of efficient algorithms to address this issue. In this manuscript, we make use of these schemes to develop a Light-Weight Convolutional Neural Network (LWCNN) for identifying diseases in the leaves and ears of pearl millets. Although many models exist in the literature, the total number of parameters employed by our model is far fewer, by an order of thousand as compared to many other light-weight networks such as MobileNet(v2), EfficientNet, NASNet etc. Hence our scheme can be employed and run directly on devices with much lesser compute power. It is noteworthy that despite using few parameters, the proposed model achieves an accuracy of 97.4% in detecting the existence of the downy mildew disease in pearl millets, and takes the least time for both training and testing as compared to other models. To eliminate most of the pre-processing steps and to make our system suitable for on-field detection, we explore three single stage object detectors namely SSD, YOLOv3 and RetinaNet which localize and classify multiple instances of healthy and diseased leaves and ears in the image. We present a comparative analysis of the models and our experiments indicate that SSD is most suitable outperforming the other two models by a significant margin.