Abstract:
Fruit diseases are a severe problem for all farmers, especially for fruit growers. A
major challenge for large farms is the spread of diseases that make the fruits unfit
for consumption, which also significantly impacts on farmer’s income. Farmers must
detect disease early in its life cycle in order to prevent it from spreading. Traditional
fruit disease detection and identification rely on a person’s ability to see the diseased
fruit. Even though this approach sufficiently caters for small-scale farmers, it requires
a high level of expertise to correctly identify the disease. Machine Learning and Image
Processing techniques have been used in recent research to develop automated solutions
for this problem. Papaya which is a popular fruit in Sri Lanka which is also having
a high postharvest loss has been considered in this study. Among various papaya
diseases, the most prevalent papaya diseases in Sri Lanka namely anthracnose, black
spot, powdery mildew, phytophthora, and ringspot were selected. Data were collected
from public image sources from the internet and from actual fields. VGG 16 as a
Convolutional Neural Network technique was used to develop a computerized model
for detecting papaya diseases. Literature reveals that many of the image-based disease
recognition systems possess limitations due to insufficient data. Therefore, it is believed
that novel data augmentation methods have promising advantages. In this approach,
Deep Convolutional Generative Adversarial Network (DCGAN) was used to develop the
data set. The VGG 16 model accuracy was found using the same pre-processed data set
with and without being subjected to DCGAN. According to the results, the VGG 16
model showed a high accuracy for all diseases. Accuracy values for Anthracnose, black
spot, powdery mildew, phytophthora, and ringspot were 90%, 85%, 70%, 65%, and 90%
respectively. The results revealed that the proposed DCGAN model outperforms the
basic data augmentation approaches.
