Potato Plant Leaf Disease Detection Using Deep Learning Method
Cemal İhsan SOFUOĞLU, Derya Birant
Abstract
In agriculture, plant disease detection is an important concern to achieve high crop production and yield sustainably. Automated detection and analysis could be beneficial for early action to prevent spreading, cure the plant in earlier stages, reduce the damage, and protect crop or forest health. This study proposes a new deep-learning model that correctly classifies plant leaf diseases for the agriculture and food sectors. It focuses on the detection of plant diseases for potato leaves from images by designing a new convolutional neural network architecture. The experimental results conducted on a real-world dataset showed that a significant improvement (8.6%) was achieved on average by the proposed model (98.28%) compared to the state-of-the-art models (89.67%) in terms of classification accuracy.