The smart farming is to deliver solutions that are revolutionary to the
question of how humankind can continue to exist in a sustainable
manner over the long stretch of time. Identification of the recorded
image is absolutely necessary in order to monitor the development of
the plant and protect it from various diseases and pests. Currently, the
objective of automatic disease recognition is to conduct research on
crop diseases through the use of deep learning. However, existing
classifiers have problems with a variety of challenges, including the
identification of appropriate disease categories, among other things.
This page is dedicated to the disease that specifically affects tomatoes
as a crop, which is known as tomato disease. The purpose of this
research is to improve the structure of tomato plant photographs for
the purpose of image identification. Because of this, the process of
extracting features from photographs of plants is more effective and
precise than the approach that is typically taken in artificial
recognition. Using three separate sets of photographs recorded by a
camera and a drone, the effectiveness of the proposed architecture was
evaluated. These images were taken in three different environments
where tomatoes are grown. Taking into consideration the statistics, this
method of counting articles achieves an accuracy rate of approximately
96.30% on average. The decision-making process in precision
agriculture is aided by the scientific support and reference it receives.
R. Swathi, K. Swasthika
Amal Jyothi Engineering College, India
Leaf Disease, CNN, MobileNets
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| Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 5 , Issue: 3 , Pages: 650 - 654 )
Date of Publication :
June 2024
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134
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27
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