POTATO LEAF DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORK WITH TRANSFER LEARNING

ANDREAS SITUMORANG, RUBEN, DANIEL RYAN HAMONANGAN SITOMPUL (2021) POTATO LEAF DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORK WITH TRANSFER LEARNING , SKRIPSI, UNIVERSITAS PRIMA INDONESIA

ABSTRAK

According to the Indonesia Central Bureau of
Statistics, the province of North Sumatra produced about
1309976 tonnes of potato in 2019-2020. Limitation of farmers
to detect plant diseases may cause crop failure. Plant diseases
commonly found in potato leaves, such as late blight and early
blight, might affect the quality of the potatoes. Early detection
of leaf diseases can help farmers prevent further damage to the
plants. This research presents a Convolutional Neural Network
model with ResNet-50 architecture that can do image
processing with a minimum accuracy of 85%. The Dataset
used in this research was from PlantVillage, which contains
2152 images and is classified to each disease. This research
achieved the actual prediction accuracy of 96,7%, according to
trials done.

PROSIDING