BITS Faculty Publications
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Item Analysis of Transfer and Residual Learning for Detecting Plant Diseases Using Images of Leaves(Springer, 2018-09) Raman, SundaresanThe 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 classesItem SEMFD-Net : A Stacked Ensemble for Multiple Foliar Disease Classification(ACM Digital Library, 2022-01) Raman, SundaresanFoliar diseases account for upto 40% to the loss of annual crop yield worldwide. This necessitates early detection of these diseases in order to prevent spread and reduce crop damage. The PlantVillage Dataset is the largest open-access database comprising 38 classes of healthy and diseased leaves. However this dataset contains images of leaves taken in a controlled environment which severely restricts the portability of models trained on this dataset to the real world. Motivated by the need to detect a variety of leaf diseases captured under diverse conditions and backgrounds, as is the case presently where many farmers do not have access to lab infrastructure or high-end cameras, we choose the PlantDoc dataset for our experiments. This dataset contains images comprising a subset of 27 classes of the PlantVillage dataset taken under different backgrounds and of varying resolutions. In this paper, we first present a new set of baselines for foliar disease classification using images taken in the field highlighting the inadequacy of current benchmarks. Secondly, we propose a Stacked Ensemble for Multiple Foliar Disease classification (SEMFD-Net), an ensemble model created by stacking a subset of our baseline models and a simple feed-forward neural network as our meta-learner which significantly outperforms the baselines.Item A low power consumption mobile based IoT framework for real-time classification and segmentation for apple disease(Elsevier, 2022-10) Raman, Sundaresan; Chamola, VinayUntreated diseases in plants not only lead to monetary losses but can have adverse implications when consumed. Disease diagnosis requires early detection and analysis of the disease. Apple horticulture has been a significant agriculture industry around the world and is affected by three most prominent domains of disease in apple namely: Blotch, Scab and Rot. In this paper, we provide a real-time mechanism for simultaneous classification and segmentation of the disease which significantly improves the speed of prediction. We have introduced atrous skip connections with UNet (with ResNet as backbone) furthering the performance. Experimental results on our proposed framework, achieves an accuracy of 94.29% to classify the disease and a dice score of 90.01% for segmentation of the diseased part. We also have developed a mobile application to demonstrate the objectives and to facilitate a user-friendly interface for using the proposed framework.