Canopy cover is the layer formed by the branches and crowns of plants or trees. The cover can be continuous, as in primary forests, or discontinuous - with gaps - as in orchards. Canopies in tropical and temperate forests can be important habitats for many animals and plants.

A dense canopy cover will let little light reach the ground and will lower temperatures. The canopy protects the ground from the force of rainfall and makes wind force more moderate. Thus, habitat conditions on the ground are shaped by the degree of canopy cover.

Canopy cover is measured as the proportion of a fixed area of the ground covered by tree crowns. The canopy cover will be determined by the tree species, as they have different crown sizes, shapes, and heights.

Tree canopy measurements are important for various reasons:

• The canopy can be affected by the health of a tree, which in turn is affected by nutrition, water access, disease, pest infestations, and stress. Thus, canopy cover acts as an indicator of these factors for both natural as well as cultivated groves and forests.

• The efficiency of fertilization, irrigation, and pruning methods in agriculture, horticulture, and floriculture also affect the canopy cover, so its measurement sheds light on these factors as well. In forests, canopy cover is also used to study the effects of fire, pollution hazards, and microclimate. The impact of forest use, in terms of deforestation, degradation, thinning or afforestation, can also be evaluated by canopy cover. The canopies of forests and other natural vegetation are important in watershed areas for replenishing groundwater and hydrology, so they need to be monitored.

Wine makers can use images captured and transmitted by satellites in orbit and enter them into geographic information system (GIS) software to generate detailed vineyard maps. The images are sharp enough to let the entire vineyard be divided into 2-meter square blocks, and the software is capable of recording elevation, slope, soil condition and water retention ability for each block. It still requires walking the vineyard to gather that information, but the result is a digital asset of enormous value in getting the most from the land. Using it, winegrowers can determine the best grape, plant spacing, arrangement of rows and irrigation or drainage for each 2-meter block.

Infrared detection from space can reveal much more. Specialized satellites beam infrared light at the ground and receive reflections. These can be analyzed to produce something called a normalized difference vegetation index (NDVI), which accurately measures the amount of leaf area in each 2-meter block. By taking repeated scans through the growing season, winegrowers can get a detailed block-by-block analysis of the all-important vigor. They can then focus their attention on blocks where there is too much or too little, and apply the time-honored practices of winegrowing to reduce or increase it. The result is lower labor cost, higher productivity and grapes of a more consistent quality year in and year out.

A modest estimate puts 75 million IoT agricultural devices in use by 2020 and 4.1 million daily data points per farm by 2050. The volume of data -- collected through technologies ranging from farm machinery to drone imagery -- is too abundant for humans to process. Farmers and agricultural technology workers are turning to AI to help analyze data points, thus enhancing value derived from these data sources.

Farms produce hundreds of thousands of data points on the ground each day. With the implementation of agricultural AI, farmers can now analyze weather conditions, temperature, water usage or soil conditions collected from their farm to inform decisions. AI technologies are helping determine the feasible crop choices or which hybrid seeds will increase profit and decrease waste.

In addition to ground data, computer vision and deep learning algorithms process data captured from drones and unmanned aircraft systems. AI combined with unmanned aircraft systems and drones can capture images of the entire farm and analyze the images in near-real time -- monitoring and analyzing soil health and condition of crops across the entire farm, as well as identifying problem areas.

Augmented reality is the blending of interactive digital elements like dazzling visual overlays, buzzy haptic feedback, or other sensory projections into our real world environments. A technology that superimposes a computer generated image on a user's view of the real world, thus providing a composite view.

Augmented reality in viticulture plays a metamorphic role in the field – check and pest/insect detection process. Moreover, it also determines the soil properties and crop to be sown in a particular piece of land. AR helps in understanding pest and crop conditions just by looking at the application. AR can also help you do any yield estimation of your plantation.

The mechanism that drives Smart Farming is Machine Learning — the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments.

Some use cases
Field conditions management

Soil management
Machine learning algorithms study evaporation processes, soil moisture and temperature to understand the dynamics of ecosystems and the impingement in agriculture.

Water Management
Water management in agriculture impacts hydrological, climatological, and agronomical balance. So far, the most developed ML-based applications are connected with estimation of daily, weekly, or monthly evapotranspiration allowing for a more effective use of irrigation systems and prediction of daily dew point temperature, which helps identify expected weather phenomena and estimate evapotranspiration and evaporation

Yield Prediction
Yield prediction is one of the most important and popular topics in precision agriculture as it defines yield mapping and estimation, matching of crop supply with demand, and crop management. State-of the-art approaches have gone far beyond simple prediction based on the historical data, but incorporate computer vision technologies to provide data on the go and comprehensive multidimensional analysis of crops, weather, and economic conditions to make the most of the yield for farmers and population.

Crop Quality
The accurate detection and classification of crop quality characteristics can increase product price and reduce waste. In comparison with the human experts, machines can make use of seemingly meaningless data and interconnections to reveal new qualities playing role in the overall quality of the crops and to detect them.

Disease Detection
Both in open-air and greenhouse conditions, the most widely used practice in pest and disease control is to uniformly spray pesticides over the cropping area. To be effective, this approach requires significant amounts of pesticides which results in a high financial and significant environmental cost. ML is used as a part of the general precision agriculture management, where agro-chemicals input is targeted in terms of time, place and affected plants.

Weed Detection
Apart from diseases, weeds are the most important threats to crop production. The biggest problem in weeds fighting is that they are difficult to detect and discriminate from crops. Computer vision and ML algorithms can improve detection and discrimination of weeds at low cost and with no environmental issues and side effects. In future, these technologies will drive robots that will destroy weeds, minimizing the need for herbicides.

Image processing can be used for pest management detection of insects, identification of nutrient deficiencies and plant content, fruits quality inspection, grading of agricultural products like fruits vegetables, crop and land estimation and object tracking.

Yes! Not only can Terraview analyse imagery of your vineyard, we also support data that comes from on-the-ground devices that sense the health of your crop.

Wine industry is using information technology as a way to more quickly adapt to the uncertainties of nature. The newest weather stations provide real-time, site-specific data, allowing growers to instantly and precisely monitor weather conditions at specific vineyard locations, including multiple locations simultaneously. They even assist vineyard developers in evaluating sites for potential development.

Identifying and tracking weather conditions in the vineyard allows growers to make informed weather-related decisions, saving time and money, and ultimately improving the quality of the fruit. More accurately detecting weather parameters and soil moisture, and acting on this information, also results in more reliable frost protection, the ability to irrigate based on demand, and, over time, the capacity to employ pest and disease models that will indicate if and when spraying is necessary.

The measurement of soil moisture is important in the evaluation and application of some vineyard management practices. Regulated deficit irrigation, partial root-zone drying, soil structure modification or mulching can be applied to improve winegrape quality or save water. Soil moisture monitoring is used to measure the effects of management practices on:

• Vine water stress;
• Vineyard evapotranspiration;
• Root system extent;
• Irrigation wetting pattern;
• Changes in soil water holding capacity;
• Rainfall infiltration
  

Terraview uses Drone and Satellite Imagery alongwith on ground sensors and passes it through its AI Platform to provide information on Soil Moisture.