Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning

Citations Over TimeTop 1% of 2017 papers

Abstract

Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVillage dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. The performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning are evaluated systemically in this paper. The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. The proposed deep learning model may have great potential in disease control for modern agriculture.

Related Papers

• → Survey of Deep Representation Learning for Speech Emotion Recognition(2021)117 cited
• → An analysis of binarization ground truthing(2010)44 cited
• → Survey of Deep Representation Learning for Speech Emotion Recognition(2021)12 cited
• → Survey of Deep Representation Learning for Speech Emotion Recognition(2021)13 cited
• → Ground-Truth Data Modeling for Gait Analysis based on Kinect V2 Sensor Data(2022)