Abstract: In order to improve early emergence stages discrimination of plants, an early emergence app is proposed based on a neural network optionally with ‘attention’ mechanisms, which detects the number of plants that opened after sowing and are present in the field. This way the farmer can easily determine, if crop density targets are met at an early stage after sowing and optionally receive recommendations on catch crop.

Abstract: Computer-implemented method and system (100) for enhancing a plant image database (230) for improved damage identification on plants. The system receives a real-world image (91) of a plant (11), recorded at a particular geographic location (2), together with image metadata comprising location data (LD1) indicating the particular geographic location (2), and a time stamp (TS1) indicating the point in time (3) when the real-world image (91) was recorded. A damage identifier (110), trained for identifying damage classes associated with damage symptoms present on plants of particular plant species, generates, from the real-world image (91), an output including a damage class (DC1) for the damage symptoms on the real-world image.

Abstract: In order to improve early emergence stages discrimination of plants, an early emergence app is proposed based on a neural network optionally with ‘attention’ mechanisms, which detects the number of plants that opened after sowing and are present in the field. This way the farmer can easily determine, if crop density targets are met at an early stage after sowing and optionally receive recommendations on catch crop.

Abstract: In order to provide an efficient recognition method for agricultural applications, a decision-support device for agricultural object detection is provided. The decision-support device comprises an input unit configured for receiving an image of one or more agricultural objects in a field. The decision support system comprises a computing unit configured for applying a data driven model to the received image to generate metadata comprising at least one region indicator signifying an image location of the one or more agricultural objects in the received image and an agricultural object label associated with the at least one region indicator.