Authors :
Aditi Maurya; Varada Nakhate; Ananya Maurya; Nuha Modak
Volume/Issue :
Volume 8 - 2023, Issue 9 - September
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/4ud39tr7
DOI :
https://doi.org/10.5281/zenodo.8354796
Abstract :
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
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
