Corn Leaf Disease Detection (The Crop Master)

Authors : Aditi Maurya; Varada Nakhate; Ananya Maurya; Nuha Modak

Volume/Issue : Volume 8 - 2023, Issue 9 - September

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Corn production is a vital component of the agricultural industry, serving critical roles in areas such as biofuel production and the global food supply chain. Moreover, it supports household industries through small-scale cultivation. However, corn crops face significant risks, including susceptibility to diseases that can severely impact agricultural yields. Furthermore, extreme weather events like cyclones and unpredictable temperature fluctuations can aggravate the spread of these diseases. Given the limitations of the human eye in detecting leaf sickness or disease, there is a pressing need for a rapid and intelligent disease detection process, utilizing advanced deep learning techniques. To address this challenge and enhance crop yield, recent advancements in smart devices have enabled the implementation of Convolutional Neural Network (CNN) models for the training and testing of corn leaf images. This innovative approach offers a time-efficient solution for the early detection of leaf diseases, ultimately strengthening the nation's support for digital agriculture.

Keywords : Convolutional Neural Network; detection; digital agriculture; leaf sickness; images


Paper Submission Last Date
31 - December - 2023

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In 1-2 Days

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