Disease Identification in Leafy Vegetables Using Transfer Learning
Abstract
Abstract: Plants are the major source which
gives foods for human to survive. In
developing countries like SriLanka
agriculture plays a major role in the
economic success of people live there and as
well as for the whole country's success. In
such a situation diseases cause huge losses to
farmers. The key concept of maintaining
quality and quantity of crops is to detect
diseases in earlier stages at the correct time
and to take preventive actions against the
disease. Usually, farmers recognize diseases
through naked eye observation. So, it may
not the right caption and it tends to spread
wrong pesticides and overdosages of
pesticides. Hiring expertise in this area is
highly costing and not possible to find that
many experts. Here include many techniques
used to identify diseases in various types of
plants. But those papers do not address the
area of Sri Lankan leafy vegetable disease
identification. This research work proposed
a system with a learning approach for
disease identification procedure named
transfer learning and fine-tuning, partially
tested, and obtain better results. InceptionV3
and VGG16 are the two pre-trained models
use to retrain the model. InceptionV3 gain
0.95 training accuracy and 0.79 validation
accuracy. VGG16 gain 0.91 training accuracy
and 0.86 validation accuracy. At the initial
stage the tested system has capable of
recognizing brown spot disease at 0.43 and
0.48 testing probabilities in Gotukola, and
leaf-spot disease at 0.58 and 0.90 testing
probabilities in the Mukunuwanna plant
through VGG16 and InceptionV3
respectively.
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