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Early blight of tomato1/29/2024 Imagenet classification with deep convolutional neural networks. Krizhevsky A, Sutskever I, Hinton, GE (2012). Kamilaris A, Prenafeta BFX (2018) Deep learning in agriculture: a survey. An open access repository of images on plant health to enable the development of mobile disease diagnostics. 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA 770–778. Deep residual learning for image recognition. Golhani K, Balasundram SK, Vadamalai G, Pradhan B (2018) A review of neural networks in plant disease detection using hyperspectral data. A robust deep-learning-based detector for real time tomato plant diseases and pests recognition Sensors 9. Comput Electron Agric 145:311–318įuentes A, Yoon S, Kim SC, Park DS (2017). Efficacy and economics of fungicides and their application schedule for early blight ( Alternaria solani) management and yield of tomato at South Tigray, Ethiopia J Plant Pathol Microbiol 6.įerentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Foldscope: origami-based paper microscope. Comput Electron Agric 157:63–76Ĭybulski JS, Clements J, Prakash M (2014). CRC Crit Rev Plant Sci 29:59–107Ĭruz A, Ampatzidis Y, Pierro R, Materazzi A, Panattoni A, Bellis LD, Luvisi A (2019) Detection of grapevine yellow symptoms in vitis vinifera L. J Theor Appl Inf Technol 95:6800–6808īock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. BTW 2017-the 17 th conference on database systems for business, technology and web, Stuttgart, Germany 79-88Ītabay HA (2017) Deep residual learning for tomato plant leaf disease identification. A deep learning based approach for banana leaf diseases classification. Finally, a case study has been conducted based on the estimated severity levels and the required fungicide treatment is also prescribed.Īdhikari P, Oh Y, Panthee DR (2017) Current status of early blight resistance in tomato – an update. Among these networks, the deep ResNet101 architecture has achieved the highest accuracy of 94.6%. The results of ResNet101 architecture is compared with other pre-trained CNN such as Visual Geometry Group16 (VGG16), VGG19, GoogLeNet, AlexNet, and ResNet50. The dataset in the model is trained by using an open database i.e., PlantVillage dataset for mild, moderate, and severely diseased leaves along with healthy tomato leaves. Further, a deep Residual Network101 (ResNet101) of Convolutional Neural Network (CNN) architecture is used to measure the severity level of early blight disease in tomato leaves. In this work, an identification of early blight disease in tomato leaves is performed by a recently invented paper microscope named Foldscope. Tomato crops are frequently affected by a dangerous fungal disease i.e., early blight, resulting in 100% production loss to farmers. An intelligent state of the art technique i.e., deep learning plays an inevitable role in most of the real-time applications including smart farming. Hence a perfect system is essential to measure the severity level of the disease in order to improve its productivity. Assessment of disease severity is one of the major challenges which helps in the prediction of yield quantitatively and to decide the control factors that improve the yield of any crop.
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