| dc.description.abstract | Coffee is one of the most widely consumed
beverages worldwide and an essential crop for many
economies However, several illnesses that might
negatively affect coffee yield and quality can affect
coffee plants. For crop losses to be kept to a minimum,
early detection of these diseases is essential. This
research suggests a technique that makes use of
convolutional neural networks (CNN). The suggested
method entails several steps. Gather a dataset of coffee
plants first, including both healthy plants and
unhealthy plants. After that, the dataset is pre processed to improve the quality of the images. The
pre-processed dataset is then used to create and train
a CNN architecture. The CNN develops the ability to
automatically recognize patterns and traits. Once
trained, the CNN model can be used to identify
diseases in coffee plants. This forecast can help
farmers and agricultural professionals spot sick plants
quickly and take appropriate action. Extensive tests
and comparative analyses are carried out to assess the
performance of the proposed method. The outcomes
show how well the CNN-based method for detecting
coffee plant diseases performs in terms of accuracy
and dependability. The suggested approach provides a
potentially effective response to the difficultiesinvolved
in manually identifying diseases. Our proposed model
with CNN three-layer classifier with a 0.01 learning
rate achieved an overall classification accuracy of
0.89% with the 28th iteration of the training process
out of a total of 100 planned epochs. This research
utilizes the capability of CNNs to construct automated
systems for identifying agricultural diseases,
ultimately assisting in sustainable coffee production,
and securing the livelihoods of coffee producers. | en_US |
