Regression in Convolutional Neural Networks applied to Plant Leaf Counting

Abstract

Recent studies have shown that computer vision techniques developed to boost the count of plant leaves brings significant improvements. In this paper, a proposal was presented for plant leaf counting using Convolutional Neural Networks (CNNs). To accomplish the training process, CNNs architectures were adapted to solve regression problems. To evaluate the proposed method, an image dataset with 810 images of three species (Arabidopsis, Tobacco and one mutation) was used. The results showed that Xception architecture obtained the best results with R2 of 0.96 and MAE (mean absolute error) of 0.46.

Code and data availability

The full-text asset investigation has not produced a conclusive result yet.

No evidence-backed public reproduction asset is currently recorded.