Volume and leaf area calculation of cabbage with a neural network-based instance segmentation
This provides a practical tool for agricultural monitoring of cabbage health and management, though it is incremental as it applies an existing method to a new crop.
The researchers developed an image-based method using Mask R-CNN to measure cabbage fruit volume and leaf area, achieving 92.6% accuracy for fruit size and 89.8% for leaf area on individual plants.
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.