DEEP LEARNING BASED WHEAT EARS COUNT IN ROBOT IMAGES FOR WHEAT PHENOTYPING

Abstract

The number of spikes, spikelets per spike, number of spikes per square meter are essential metrics for plant breeders and researchers in predicting wheat crop yield. Evaluating the crop yield based on wheat ears counting is still done manually, which is a labor-intensive, tedious and costly task. Thus, there is a significant need to develop a real-time wheat spikes/ears counting system for plant breeders for effective and efficient crop yield predictions. This paper proposed two deep learning-based methods based on EfficientDet and Faster-RCNN to detect and count the spikes. The images are taken using high-throughput phenotyping techniques under natural field conditions, and the algorithms localize and automatically count wheat spikes/ears. Faster R-CNN with Resnet50 as backbone architecture produced an overall accuracy of 88.7% on the test images. We also used recent state-of-the-art models EfficientDet-D5 and EfficientDet-D7, having backbone architectures EfficientNet-B5 and EfficientNetB7, respectively. A comprehensive quantitative analysis is performed on the standard performance metrics. In the analysis, the EfficientDet-D5 model produces an accuracy of 92.7% on the test images, and EfficientDet-D7 produces an accuracy of 93.6%.