Technologies advance at a rapid pace and innovations in the area of Integrated Weed Management are increasingly available to silvicultural operations. The country stands out in the production of products derived from planted forests.

With tree production estimated at 10 million hectares, with eucalyptus being the most cultivated species (76% of the planted area in the country), followed by Pine (19%) and other species (5%), according to data from the Brazilian Tree Industry. However, the extensive cultivation areas associated with different biomes bring challenges in managing weed competition, especially with regard to quantifying weed species present in plantations, infestation levels and the damage potential of competing vegetation.

Competition for space, light, water and nutrients significantly affects the development of tree species, reducing the availability of these growth factors and forest productivity. The cost of management to avoid weed competition is among the biggest investments in forestry, due to the diversity of species, the rapid growth of plants, morphological and physiological characteristics, the variability in terms of tolerance to different control methods, as well as related aspects herbicides and their application technology.

In this sense, digital weed monitoring technologies emerge as important tools for decision-making within integrated management. Monitoring weed competition in forest areas is an essential step in management management. Weed control is crucial for the growth of the forest, but identifying them directly is a step that requires professionals to cover the entire area, not being a quick identification and often without the possibility of effectively identifying their presence, the density and importance value of weed species in the area. New techniques are being studied, validated and applied to monitor competing vegetation in planted forests, for example, using satellites and/or drones.

With the use of machine learning algorithms and statistical analysis, weed patterns are being known, making it possible to predict infestation trends and optimize weed competition management, in addition to being able to be used to evaluate the effectiveness of the management carried out. Thus, decision-making can be carried out accurately, minimizing errors in weed management. The use of automated technologies and robotics is becoming increasingly common in precision forestry, as is the use of drones to assess hard-to-reach areas. The use of unmanned aerial vehicles allows detailed information to be obtained on a centimeter scale in real time.

Sensors make it possible to collect detailed information about forest characteristics quickly, accurately and on a large scale. Remote sensing is a technique that uses sensors mounted on aerial platforms (such as satellites, planes or drones) to collect data about forests. These sensors capture information at different wavelengths, creating high-resolution images. The RGB model is the most common of the color spaces, being expressed by the combination of the colors of the red (R), green (G) and blue (B) bands, considered visible to human eyes, within their respective lengths, blue being (450 to 495 nanometers), green (495 to 570 nanometers) and red (620 to 750 nanometers.

Another widely used color space is infrared (NIR), which identifies a spectrum of area considered not visible to human eyes and is between 760 and 2500 nanometers. Using the wavelengths of each band, mathematical calculations (spectral indices) are possible with the aim of detecting target characteristics. The normalized difference vegetation index, one of the most widely used spectral indices in vegetation assessment, uses the difference between near infrared (NIR) and red to measure the amount of chlorophyll in leaves and correlate with the photosynthetic activity of plants.

In forest management, the normalized difference vegetation index can be used to monitor physiological characteristics of species, their identification, estimate plant biomass and obtain physical parameters of plants. The leaf area index measures the amount of foliage in a given area of vegetation. It can be calculated from reflectance data from various spectral bands and is useful for assessing forest cover density, plant productivity, plant response to different environmental conditions and weed infestation.

Other spectral indices are also used to understand the temporal behavior of the vegetative vigor of species and their interaction with electromagnetic radiation, such as the Enhanced Vegetation Index, Soil-Adjusted Vegetation Index, Green band Normalized Difference Vegetation Index, Index of Chlorophyll, Simple Ratio Vegetation Index, among others.

The application of Radar and Synthetic Aperture Radar data is an advanced tool for monitoring weed competition and weed identification. Synthetic aperture radar images are obtained by orbital sensors and their analysis is based on the principle of synthetic aperture radar, which, through antennas, emits radar pulses towards forests with penetration and coverage in adverse weather conditions. Unlike optical images which are affected by weather conditions.

Another tool also used for mapping weeds in forest plantations is LiDAR, an active remote sensor on board platforms (manned or unmanned). Data capture is carried out directly, with its own energy source (laser). LiDAR emits laser beams in the near - infrared (NIR) band and allows three-dimensional modeling of the terrain surface. The Digital Terrain Model and the Digital Surface Model are products generated mainly for topographic surveys that allow the characterization of the structure of forests.

The use of forest maps associated with the use of spectral indices is an agile tool for evaluating and monitoring species characteristics. Digital models using remote sensing are important tools for evaluating the dynamics of forest ecosystems, predicting trends and making decisions for managing areas. However, the models may have limitations and their effective applications require validation with field data.

Artificial intelligence is becoming an increasingly important tool in the forestry sector, offering a wide range of applications to improve forest management, including weed detection. Machine learning algorithms are capable of identifying complex patterns and making accurate predictions based on this information. Identification of tree species and weeds can be obtained using artificial intelligence, as a very useful tool for forest inventories.

Recent studies, in different countries, have demonstrated the efficiency of machine learning using neural networks and deep learning to select the best learning method. The spectral behavior of planted forests has contributed to the identification and differentiation of eucalyptus, pine and weed trees. Better weed control, the correct positioning of herbicides and more precise management are part of the new management of Brazilian planted forests and are driving the sector to become increasingly technological.