Recognizing Wheat Aphid Disease Using a Novel Parallel Real-Time Technique Based on Mask Scoring RCNN

Abstract

Wheat is one of the most common cereal crops in India. Aphids cause extensive damage to the whole wheat plant and lead to high yields loss. The aphid is transmitted on the summer day. Once the aphid is transmitted over the leave, the whole plant leave is damaged. Due to this, got damage whete plant and it also reduce the quality of wheat grain. Due to which the it is necessary to identify each aphid on wheat leaves. Only manual process is there to identify each aphid on wheat leave. Manually identification is a time-consuming and high laborious process. Therefore, the identification of wheat aphids through the Mask RCNN model can easily identify the location of each aphid in individual leave automatically. With the help of a Canon camera, a total of 6500 wheat images have been captured in the Punjab region with temperature 21–24° temperature. The Labelme software is used for the annotation of wheat leaves and wheat aphid. A total of 2300 and 1000 images have been randomly selected for training and testing purposes. The Mask scoring RCNN model is having a network capacity for learning the quality of predicted instance masks. Among all 1221 wheat leaves, a total number of 1021 wheat aphids have been found. The manually annotated ROI was compared to mask scoring ROI for wheat aphid identification and localization. Thus, the Mask scoring RCNN model has been achieved a high F1-score (96.66 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) for wheat aphid detection in single wheat leave.