An image analysis technique for wheat head count detection using machine learning - Kalluri, Ramadevi; Selvaraj, Prabha (Bulletin of Electrical Engineering and Informatics, 2024)
Deep learning (DL) techniques have significantly enhanced the potential for wheat head detection in recent times. The different development phases of canopy, genotype, wheat heads, and wheat head orientation provide considerable obstacles. The overlapping density of wheat heads and wind- induced picture blurring complicate wheat head recognition. This study describes an effective wheat head detection and counting method. Due to its high throughput in agricultural field analysis, remote sensing phenotyping has grown in popularity. Applying DL methods for image processing and other technological advancements has increased the scope for the quantitative evaluation of various crop traits. The ability to detect and characterize wheat heads in the industry is an important part of the wheat breeding process for selecting high-yielding cultivars. The proposed method uses the Mask region-based convolutional neural network (RCNN) framework to detect and classify the wheat ears. The complete detection task is done in three steps: region proposal generation, region of interest alignment, and mask generation. The global wheat head detection (GWHD) dataset is used for the experimental analysis of the dataset. The proposed method achieved an accuracy of 95.11% on the GWHD dataset, demonstrating its effectiveness in wheat head detection and classification tasks.