A Comprehensive OrthoMosaic-Based Pipeline for Enhanced Automated Wheat Ear-Head Detection and Crop Yield Estimation

Abstract

Accurate identification and counting of wheat ear-heads are critical for reliable crop yield estimation. This study presents a novel pipeline utilizing the YOLOv8 model specifically designed for detecting wheat ear-heads in challenging agricultural environments. Leveraging orthomosaic imagery captured by drones, our approach integrates several advanced techniques to enhance detection accuracy. The pipeline begins with the identification of plots from orthomosaic images, followed by extraction and tiling of these plots for detailed analysis. The detection model is applied to the tiled images, and the results are stitched together to reconstruct the original image annotated with detected wheat ear-heads. To improve image quality—addressing issues like brightness, contrast, exposure, and blur—we employed the Real ESR GAN technique alongside a random cut out method for effective occlusion handling. Evaluations on Mahyco's dataset demonstrated a mean average precision (mAP) of 99.2% for plot detection and an accuracy of 86.7% for wheat ear-head detection against ground truth. Our model exhibited robust performance across varying growth stages and adverse conditions, underscoring its potential for practical agricultural applications. This research introduces an end-to-end pipeline that automates wheat ear-head detection, enabling scalable in-season yield prediction and providing a valuable tool for farmers and agronomists to enhance crop management decisions. Furthermore, our pipeline can be adapted into a desktop app or web portal, allowing farmers to upload orthomosaic images and receive an output Excel file with plot numbers and corresponding wheat ear-head counts, thus enabling comprehensive wheat yield estimation for their farmland.

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