Using terrestrial photogrammetry for leaf area estimation in maize under different plant growth stages

Reiser, David; Kamman, A.; Arellano, Manuel Vázquez; Griepentrog, Hans W.

doi:10.3920/978-90-8686-888-9_41

Cited by 4 publications

(3 citation statements)

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“…Due to its cost-effectiveness and versatility, depth-information acquisition using an SfM approach is widely used for agricultural applications [ 14 , 40 ]. The linear movement of the camera at a constant height and speed, the short distance between the camera and the object, and the static environment offer good conditions for an SfM method.…”

Section: Methodsmentioning

confidence: 99%

“…This method, however, is very time consuming and will cause damage to crops such as cabbage due to its sensitive leaves. The most common method of measuring leaf area and crop volume is probably the manual measurement of plant parameters such as leaf length, leaf width, and crop diameter with a measuring tape [ 14 ]. The measured parameters are then used with a formula to determine the fruit volume, for example.…”

Section: Introductionmentioning

confidence: 99%

“…To record plant parameters such as leaf area or fruit volume, sensor-based depth information can be used, giving the two-dimensional masks of the instance segmentation a three-dimensional shape. Attempts using different kinds of depth sensors [ 14 , 32 ] were presented, to obtain information about the LA. Depending on the application, there are several factors influencing the choice of sensor technology for depth-image acquisition.…”

Section: Introductionmentioning

confidence: 99%

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Fruit Volume and Leaf-Area Determination of Cabbage by a Neural-Network-Based Instance Segmentation for Different Growth Stages

Lüling

Reiser

Straub

et al. 2022

Sensors

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Fruit volume and leaf area are important indicators to draw conclusions about the growth condition of the plant. However, the current methods of manual measuring morphological plant properties, such as fruit volume and leaf area, are time consuming and mainly destructive. In this research, an image-based approach for the non-destructive determination of fruit volume and for the total leaf area over three growth stages for cabbage (brassica oleracea) is presented. For this purpose, a mask-region-based convolutional neural network (Mask R-CNN) based on a Resnet-101 backbone was trained to segment the cabbage fruit from the leaves and assign it to the corresponding plant. Combining the segmentation results with depth information through a structure-from-motion approach, the leaf length of single leaves, as well as the fruit volume of individual plants, can be calculated. The results indicated that even with a single RGB camera, the developed methods provided a mean accuracy of fruit volume of 87% and a mean accuracy of total leaf area of 90.9%, over three growth stages on an individual plant level.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%