Volume and leaf area calculation of cabbage with a neural network-based instance segmentation

TL;DR

This paper presents a neural network-based image analysis method for accurately measuring cabbage fruit volume and leaf area, aiding plant health assessment and management.

Contribution

It introduces a Mask R-CNN approach for segmenting cabbage parts and estimating their size and area with high accuracy from single-camera images.

Findings

Fruit size measurement accuracy of 92.6%

Leaf area estimation accuracy of 89.8%

Method effective with a single camera

Abstract

Fruit size and leaf area are important indicators for plant health and are of interest for plant nutrient management, plant protection and harvest. In this research, an image-based method for measuring the fruit volume as well as the leaf area for cabbage is presented. For this purpose, a mask region-based convolutional neural network (Mask R-CNN) was trained to segment the cabbage fruit from the leaves and assign it to the corresponding plant. The results indicated that even with a single camera, the developed method can provide a calculation accuracy of fruit size of 92.6% and an accuracy of leaf area of 89.8% on individual plant level.