Identification Of Downy Mildew And Anthocnnose in Verduries Using Deep Learning Fused Architecture of Mobile-Net and Res-Net

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Dr. K.V. Sobha Rani, Dr. CH.V.Siva Ram Prasad, D.Naga Tej, Dr Subba Rao Polamiri

Abstract

Abstract- Knowing how to treat verdure diseases is essential to preserving the health and caliber of your crops and verdures. In order to identify verdure illnesses, one must be aware of the warning signs. Infections can affect the growth, function, color, and appearance of verdures. When you see the first signs, you can intervene quickly and treat the patient effectively. Deep learning (DL), using cutting-edge technology like machine learning (ML), can assist in overcoming obstacles by facilitating early mildew identification. The study also addresses the challenges and restrictions of using ML and DL in the identification of verdures, such as issues with data accessibility, picture clarity, and differentiating between healthy and mildewed verdures. For those working on verduresmildew detection, the study provides important insights, such as answers to these problems, a comprehensive picture of the state of the field, a review of the benefits and drawbacks of these methods, and recommendations for resolving application-related difficulties.

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