Abstract
Intercropping systems of cereals and legumes have the potential to produce high yields in a more sustainable way compared to sole cropping systems. Their agronomic optimization remains a challenging task given the numerous management options and the complexity of interactions between the crops. Efficient methods for analyzing the influence of different management options are needed. The canopy cover of each crop in the intercropping system is a good determinant for light competition, thus influencing crop growth and weed suppression. Therefore, this study evaluated the feasibility to estimate canopy cover within an intercropping system of pea and oat based on semantic segmentation using a convolutional neural network. The network was trained with images from three datasets during early growth stages comprising canopy covers between 4% and 52%. Only images of sole crops were used for training and then applied to images of the intercropping system. The results showed that the networks trained on a single growth stage performed best for their corresponding dataset. Combining the data from all three growth stages increased the robustness of the overall detection, but decreased the accuracy of some of the single dataset result. The accuracy of the estimated canopy cover of intercropped species was similar to sole crops and satisfying to analyze light competition. Further research is needed to address different growth stages of plants to decrease the effort for retraining the networks.
Highlights
Intercropping systems comprise two or more crop species grown on the same field with overlapping growth periods [1]
The network trained on all datasets (LC + intermediate cover (IC) + HC) showed almost equal performance and even slightly increased the performance when applied on the intermediate and high cover datasets
The transfer of LC onto the intermediate cover dataset and IC onto the low cover dataset yielded in a mean Intersection over Union (mIoU) higher than 69%
Summary
Intercropping systems comprise two or more crop species grown on the same field with overlapping growth periods [1]. It is widely practiced in developing countries under resource-limited conditions and recently gained more interest in European countries as well, especially in organic agriculture [2,3]. External inputs can be decreased such as synthetic fertilizers and herbicides due strong weed suppression by the cereal [2,6] In addition to these numerous advantages, the agronomic optimization remains a challenging task given the large number of possible crop and cultivar combinations, spatial and temporal arrangement, and management [7,8]. To cope with the complexity of intercropping systems and their adoption by farmers, efficient methods are needed that
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have