Abstract
Rapid and automated identification of blight disease in potato will help farmers to apply timely remedies to protect their produce. Manual detection of blight disease can be cumbersome and may require trained experts. To overcome these issues, we present an automated system using the Mask Region-based convolutional neural network (Mask R-CNN) architecture, with residual network as the backbone network for detecting blight disease patches on potato leaves in field conditions. The approach uses transfer learning, which can generate good results even with small datasets. The model was trained on a dataset of 1423 images of potato leaves obtained from fields in different geographical locations and at different times of the day. The images were manually annotated to create over 6200 labeled patches covering diseased and healthy portions of the leaf. The Mask R-CNN model was able to correctly differentiate between the diseased patch on the potato leaf and the similar-looking background soil patches, which can confound the outcome of binary classification. To improve the detection performance, the original RGB dataset was then converted to HSL, HSV, LAB, XYZ, and YCrCb color spaces. A separate model was created for each color space and tested on 417 field-based test images. This yielded 81.4% mean average precision on the LAB model and 56.9% mean average recall on the HSL model, slightly outperforming the original RGB color space model. Manual analysis of the detection performance indicates an overall precision of 98% on leaf images in a field environment containing complex backgrounds.
Highlights
And late blight diseases are a common occurrence across regions where potato (Solanum tuberosum L.) is cultivated
Even within each color space, there are two types of Mask R-convolutional neural network (CNN) models: (i) Two-class model: this involves the detection of only potato blight patches, while the rest of the image is considered as background
This work has demonstrated a potato blight detection model using the deep learning approach that can be applied in field conditions, for aiding the farmer in making real-time decisions
Summary
And late blight diseases are a common occurrence across regions where potato (Solanum tuberosum L.) is cultivated. Detection and identification of blight are performed manually by trained personnel scouting the field and inspecting potato foliage. This process is tedious and in some cases impractical, due to the unavailability of a disease expert in remote regions [4]. Using 300 images as a training set, another work [3] has attempted potato disease detection using segmentation and multiclass support vector machine. These datasets do not incorporate time-varying illumination and are usually taken at a fixed time corresponding to the best illumination. Methods developed using small datasets do not perform well in field environments due to the large variations in
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