Project 1

PROJECT SP1: Development and validation of new technologies of remote sensing and machine learning applied to smart weed control (DECIMAL)

This project envisages the use and validation of unmanned aerial vehicle- (UAV-) based technologies and powerful analysis procedures (machine/deep learning) for the early and accurate identification of the main weed species affecting three of Spain’s main crops (maize, vine and tomato). The results will provide information of high technological and agronomic value to feed a decision support system (DSS) and to apply more timely, cost-efficient and sustainable crop protection strategies in various agroecosystems in Spain. The following objectives will be addressed:

  1. Identifying and mapping major weed species in maize and tomato by combining UAV images and machine/deep learning procedures.
  2. Seasonal monitoring of invasive weeds (e.g., Amaranthus palmeri) in maize fields by both UAV and satellite remote sensing and evaluation of its spatio-temporal spread. Amaranthus palmeri) en parcelas comerciales de maíz mediante teledetección con UAV y satélite, y evaluación de su propagación/distribución espacio-temporal.
  3. Design and validation of a DSS for SSWM in maize based on species-specific weed maps, crop vigour maps in early season and crop yield at harvest.
  4. Assessing spatio-temporal dynamics of major weed species in vineyards under different weed management systems using UAV images and geo-referenced field data
  5. Detecting and evaluating weeds-vineyard competition problems by monitoring vines vigour in weed infested vineyards using UAV-based 3D crop models.
  6. High-throughput vineyard phenotyping by assessing vine development and phenology metrics using UAV technology.

These objectives are part of the overall objectives of the coordinated project, involving a series of necessary collaborations concerning the development of a system for the detection and identification of noxious weed species in maize and tomato with SP3, the fine-tuning of a detection system for invasive species such as A. palmeri with SP2, the design of the DSS in maize with SP2, and the implementation of alternatives to chemical weed control using plant cover in vineyard with SP2. A. palmeri con el SP2, el diseño de un sistema DSS en maíz con el SP2, y la aplicación de alternativas al control químico de malas hierbas utilizando cubiertas vegetales en viñedo con el SP2.