Agriculture is the backbone and plays a vital role in
many Asian countries. Farmers mainly depend on their
agricultural produce for their living. A report says one-third of the
farmers income account’s for the agricultural loss which is
primarily due to plant diseases. To combat this farmers are in need
of a early plant disease identification mechanism. Observation of
individual plants in the farm for detecting the disease is
labor-intensive and time consuming work, if the farm is vast and
multiple plants are cultivated then it’s even worse. To solve such
issues, current technologies like the Internet of Things (IoT) and
artificial intelligence (AI) and Machine Learning (ML) are used
to predict the diseases more effectively. Farmers usually detect
plant diseases with the help of images captured manually and
analyzed separately by experts. The proposed system renders an
efficient solution for detecting multiple diseases in several plant
varieties. The system is designed to detect and recognize several
plant varieties, specifically pepper, grapes, and strawberry. The
proposed system discovers various plant’s various diseases based
on the inputs obtained by capturing images from a built-in camera
present in the Autonomous rover. The rover also record’s it’s GPS
location and makes a map of the entire farm traced and checked
by the robot. The images are processed and are classified into their
respective categories using deep learning algorithms.
Convolutional neural networks the powerful methodology for
image classification is the underlying principle applied. The deep
learning model’s architecture namely, VGG16 and
InceptionResNetV2, are used to train the model. These models are
primarily made of convolutional layers. On testing, we recorded
am accuracy of 93.21% was obtained from VGG16, and 95.24%
from InceptionResNetV2.
A Ring Oscillator-Based Identification Mechanism Immune to Aging and External Working Conditions
