PROJECT SP4: Optimimizing crop productivity for digitazing applications: integration of architecture and spectral information (PRODIGIA)
To create opportunities that facilitate the digital transformation of large agricultural holdings, and also of small and medium-sized farms, it is essential to further develop information on gathering and processing procedures and make them more accessible to end-users. To meet this challenge, we must adapt agricultural decision-making to real needs, which entails characterizing within-field spatial heterogeneity by means of effective, non-destructive methods at competitive prices.low-costThe experience that the SP4-Group has acquired in the use of UAV technology, photogrammetric image analysis, and machine y deep learning,and interpretation of results covering a wide range of crops, enables it to focus its working hypothesis on conceivably establishing a set of open-source prototype procedures that include 3D-architecture and spectral information for digital and integral crop management. This initial hypothesis is based on our previous findings that have shown that: a) efficiency and sustainability of herbicide applications depend on the spatial distribution of weeds(between- y fuera de las líneas de siembra), su composición (control de monocotiledóneas- vs di-) y persistencia de las infestaciones, y 2) área, volumen y densidad foliares de cada individuo de un cultivo leñoso (árbol o cepa) influyen en el grado de solapamiento de su estructura vegetal y su exposición a la radiación directa viéndose afectadas cosecha esperada y programación de actuaciones a realizar (e.g., tipo de poda, aplicaciones foliares de fitosanitarios, momento de recolección, diferenciación entre variedades del cultivo, secano vs riego).
Our aim has a double actuation. We plan to be able to determine the type of herbicide and where to apply it according to the location, composition and persistence of the weed infestation, and to provide diverse information on the canopy architecture of each individual (olive tree or grapevine) which is then adjusted and transferred to meet different objectives both in hedgerow olive groves (porosity and its effect on production) and vineyards (LAI estimation and its relation to yield). SP4-Group aims to generate a set of technological and data analysis protocols that contain 3D and spectral information through free software to monitor within-field spatial heterogeneity in herbaceous and woody crops to enhance decision-making and productivity. The overarching objective is to zone and optimize crop management and ultimately advance in the digital transformation of agriculture through two global purposes which are integrated by several specific objectives:
- Monitoring weed species in annual wide-row (sunflower and cotton) and woody (hedgerow olive and vineyard) crops by UAV-imagery and low-cost sensors for site-specific management and digitizing applications.
- Early between- and within- crop-row monocotyledonous vs di- weed detecting and mapping in sunflower and cotton crops: generation of site-specific control maps according to weed species or groups.
- Studying the persistence of the major problematic perennial weed Cynodon dactylon (bermudagrass) in organic vineyard. Multi-temporal detection and mapping. Cynodon dactylon (grama)en viñedo ecológico. Seguimiento multitemporal.
- Detecting and mapping Cynodon dactylon in organic vineyard: to evaluate the efficacy of roller crimper and different cover-crops (in coordination with SP2).
- Detecting and mapping Conyza bonariensis in non-organic vineyard. Generation of site-specific treatments (in coordination with SP2).
- Detecting and mapping the major problematic weed species in hedgerow olive orchards. Ecballium elaterium): between rows; Conyza sppwithin (intra) rows. Generation of site-specific treatments.
- 3D-Monitoring of hedgerow olive groves (different cultivars) and vineyards by UAV-imagery and sensors with different spectral range (RGB, Multi-, Hiper-) for yield estimation, and oliviculture and viticulture digitizing applications.
- Estimating hedgerow porosity according to different cultivars for spatial analysis of shadowing and intercepted within-hedgerow radiation effect on yield and early bearing.
- LAI in vineyard: Radiometric characterization and estimation of leaf-area-index (LAI) in vineyard for yield estimation. Exploring hyperspectral-UAV-imagery.