Ultra-high-resolution UAV-imaging and supervised deep learning for accurate detection of in potato fields.

is the second most devastating foliar pathogen of potato crops worldwide, causing premature defoliation of the plants. This disease is currently prevented through the regular application of detrimental crop protection products and is guided by early warnings based on weather predictions and visual observations by farmers. To reduce the use of crop protection products, without additional production losses, it would be beneficial to be able to automatically detect in potato fields.

Hyperspectral Classification of Clones and Morphologically Similar Weeds.

(yellow nutsedge) is one of the world's worst weeds as it can cause great damage to crops and crop production. To eradicate early detection is key-a challenging task as is often confused with other and displays wide genetic variability. In this study, the objective was to classify clones and morphologically similar weeds.

Development of a Stereovision-Based Technique to Measure the Spread Patterns of Granular Fertilizer Spreaders.

Centrifugal fertilizer spreaders are by far the most commonly used granular fertilizer spreader type in Europe. Their spread pattern however is error-prone, potentially leading to an undesired distribution of particles in the field and losses out of the field, which is often caused by poor calibration of the spreader for the specific fertilizer used. Due to the large environmental impact of fertilizer use, it is important to optimize the spreading process and minimize these errors.