2

Field-scale crop monitoring is essential for agricultural management and policy making for food security and sustainability. Automating crop classification process while elaborating a workflow is a key step for reliable and precise crop mapping.

Cover crops play a critical role on conservation and sustainable agriculture due to their well-documented benefits on both soil and crop productivity. Inclusion of legumes (e.g., hairy vetch, Vicia villosa Roth) in the farming system can reduce the nitrogen (N) fertilizer needs for cereals such as maize (Zea mays L.) crop while maintaining or increasing its productivity.

The low N use efficiency of vines calls for more efficient fertilization processes, which must mitigate losses caused by volatilization. Thus, N dose applications through different methods, such as fertigation, can potentiate N harnessing by vines.

The spatial variability of soil organic matter, plant water availability, and soil nitrogen (N) availability are drivers of crop response to mineral N fertilizer. The complex interaction of these factors is responsible for within-field corn and wheat yield variability. The hairy vetch (HV) cover crop is an economic, environmentally friendly, and efficient N source capable of conciliating crop yield and soil health. Nevertheless, the HV effects to mitigate yield gap of management zone (MZ) have not yet been documented.

Several vegetation indices (VIs) have been proposed in the literature with the objective of exploring the spectral properties of the vegetation as they are usually related to biophysical parameters of the plants.

The presence of weeds in agricultural areas depends, among other factors, on the management system.

Soybean yield predictions in Brazil are of great interest for market behavior, to drive governmental policies and to increase global food security. In Brazil soybean yield data generally demand various revisions through the following months after harvest suggesting that there is space for improving the accuracy and the time of yield predictions.

The principal weeds in wheat cultivation are black oats and ryegrass and their control is generally performed without considering the spatial variability of the density of weed infestation. One way to identify weed species is by analyzing spectral curves of the targets.

Ao longo dos últimos anos a principal planta daninha da cultura do trigo tem sido o azevém, a qual infesta lavouras e compete com a cultura por recursos naturais. O levantamento das plantas daninhas, de forma geral, é realizado de forma indiscriminada, não levando em consideração sua variabilidade espacial. Uma forma para realizar o levantamento das plantas daninhas de forma precisa é utilizando malhas amostrais.

Site-specific weed management (SSWM) demands higher resolution data for mapping weeds in fields, but the success of this tool relies on the efficiency of optical sensors to discriminate weeds relative to other targets (soils and residues) before cash crop establishment. The objectives of this study were to (i) evaluate the accuracy of spectral bands to differentiate weeds (target) and other non-targets, (ii) access vegetation indices (VIs) to assist in the discrimination process, and (iii) evaluate the accuracy of the thresholds to distinguish weeds relative to non-targets for each VI using training and validation data sets.