A Monitoring System for the Segmentation and Grading of Broccoli Head Based on Deep Learning and Neural Networks.

Abstract

Achieving the non-contact and non-destructive observation of broccoli head is the key step to realize the acquisition of high-throughput phenotyping information of broccoli. However, the rapid segmentation and grading of broccoli head remains difficult in many parts of the world due to low equipment development level. In this paper, we combined an advanced computer vision technique with a deep learning architecture to allow the acquisition of real-time and accurate information about broccoli head. By constructing a private image dataset with 100s of broccoli-head images (acquired using a self-developed imaging system) under controlled conditions, a deep convolutional neural network named "Improved ResNet" was trained to extract the broccoli pixels from the background. Then, a yield estimation model was built based on the number of extracted pixels and the corresponding pixel weight value. Additionally, the Particle Swarm Optimization Algorithm (PSOA) and the Otsu method were applied to grade the quality of each broccoli head according to our new standard. The trained model achieved an Accuracy of 0.896 on the test set for broccoli head segmentation, demonstrating the feasibility of this approach. When testing the model on a set of images with different light intensities or with some noise, the model still achieved satisfactory results. Overall, our approach of training a deep learning model using low-cost imaging devices represents a means to improve broccoli breeding and vegetable trade.

Plant phenotyping relevance

ブロッコリー頭部の画像取得、セグメンテーション、収量推定、品質等級化を中核とする画像ベース表現型計測手法の開発・検証である。

Code and data availability

The paper's broccoli-head image dataset is only available on request to the corresponding author, and no author analysis code, trained model, or public data deposit is provided. The only public URL mentioned (labelme) is a generic third-party annotation tool, not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.