Lodging is a key factor, which affect yield and quality of wheat. Timely and accurate acquisition of wheat lodging information is conducive to lodging identification and breeding of improved varieties. In this study, based on the UAV visible light image obtained during the wheat filling period, the wheat lodging image data set was constructed, and the ACSNet network model was selected as the main model of segmentation task. The model can effectively extract local context information features. The result show that the prediction accuracy of ACSNet model was 87.5 %, which could accurately and efficiently complete the automatic classification of wheat lodging. It had high accuracy and applicability, and provided an important basis for agricultural insurance companies to identify wheat lodging and determine agricultural losses.
As an important part of breeding research, germplasm resources play a fundamental role in maize breeding. This study starts from the needs of corn breeding scientific research, according to modern breeding technology, using computer technology and systems engineering technology, to study the key technologies of corn breeding process informatization, and build a practical and reliable corn breeding intelligent auxiliary decision support system. The establishment of this system will help to mine massive germplasm resources information, shorten the breeding time of new varieties with high quality and high yield, promote the development of informatization and intelligence in the breeding process, improve the level of breeding technology, and improve the level of corn breeding in the future. and provide a reference basis for the utilization of excellent maize germplasm resources.
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