Abstract:
"The economic growth of Bangladesh is heavily
dependent on agricultural production, but the several
diseases have significantly impeded the growth of crops.
The zucchini plant is commonly afflicted by diseases such
as alternaria blight, anthracnose, and angular leaf spot. As
a result, it is now crucial to detect leaf diseases at an early
stage to prevent damage to the entire crop. However,
farmers often lack of sufficient knowledge regarding leaf
diseases and resort to manual methods for identifying
disorders. The accuracy of detection is inadequate and time
consuming. Therefore, it is crucial to develop an automated
and precise identification system to solve this issue. This
article introduces a new method for diagnosing and
categorizing diseases in zucchini plants. Deep learning,
which is a modern and effective approach, is suggested as a
means to recognize the disorder and determine the
appropriate treatment. Our primary focus was on training
the raw dataset using the CNN algorithm, which resulted in
an accuracy rate of 88.30 percent. Detecting and identifying
diseases in the zucchini plant would contribute to the
economic growth of Bangladesh by enhancing the
production rate of the crop."
