The implementation and effectiveness of geographic information systems and location intelligence technology in digital agriculture

By 2050, the world population is expected to be close to 10 billion. So to meet demand, we would need to produce almost 50% more food than we did in 2013.

Recent advances in technology have enabled us to significantly increase agricultural production to feed the world’s rapidly growing population. The fourth agricultural revolution has begun thanks to advances in digital technologies, but there are still many obstacles to overcome, including lack of arable land, dwindling water supplies and climate change. Therefore, we must take action to ensure the resilience of agriculture to feed the world population. Geographic Information System (GIS), in conjunction with other partner technologies such as remote sensing, global positioning system, artificial intelligence, computer systems and data analysis, plays a crucial role in monitoring crops and employing the best and most targeted management practices put in place to increase crop productivity .

GIS in agriculture are mainly used to analyze the land, visualize field data on a map and then use that data. Precision farming supported by GIS and GNSS technologies helps farmers make informed decisions and take appropriate actions to maximize yields per hectare while minimizing environmental impact.

Tools that use geospatial technology in agriculture include satellites, airplanes, drones, and sensors. These tools can be used to create images and link them to maps and non-visualized data. As a result, you will receive a map with information on topography, soil type, fertilizer, plant status, health and other topics.

Geospatial has a number of applications in agriculture. Let’s explore some of them.

crop yield forecast

Governments can ensure food security through accurate yield forecasts, and businesses can forecast revenues and create budgets. These predictions are possible thanks to recent technological advances linking satellites, sensors, big data and AI.

Convolutional neural networks (ConvNets or CNNs) are among the most profound methods in this field. A deep learning algorithm called ConvNet is trained to detect a crop’s productivity. To uncover productivity patterns, developers train this system by feeding it photos of plants whose yields are already known. The accuracy rate for CNN is approximately 82%.

Plant Health Monitoring

The least effective method is to manually inspect the health of numerous hectares of crops. In agriculture, remote sensing and GIS work together to solve this problem.

To assess environmental variables across the field, such as B. humidity, air temperature, surface conditions and others, satellite photos and input data can be combined. GIS-based precision farming can improve such an assessment and help you determine which crops need further care.

An advanced method of monitoring crop temperature uses image sensors on satellites and aircraft. If the temperature is higher than usual, it could be a sign of disease, infestation, or insufficient watering.

Plant health can also be determined using neural networks such as CNN, Radial Basis Function Network (RBFN), Perceptron and others. The computers can look for unhealthy trends in photos.

Livestock Monitoring

Tracking specific animal movements is the easiest use of farm GIS software in animal husbandry. This allows farmers to locate them on a farm and keep track of their diet, fertility and health. Trackers attached to animals and a mobile device that receives and displays data from those trackers are two GIS services that make this possible.

let me give you one You should keep an eye on the weight of your beef cattle. Each animal has a tracker on its neck or ear. The digital scale scans the animal’s ID every time it steps on it and updates the value of the ID in the system.

This information does not have to be entered manually. You can instantly locate the animal and assess its health if its weight suddenly changes in a worrying way.

Other intriguing uses of agricultural GIS software include avoiding encounters between wolves and cattle. The site-specific distribution of wildlife, especially wolves, is influenced by confounding spatial features. Understanding these tiny details that could be achieved by combining the use of AI and GIS in agriculture could help us reduce unfavorable interactions.

Insect and pest control

Agriculture suffers significantly from infestation with dangerous insects and pests. A bird’s eye view can help create accurate and timely alerts to stop this.

However, even high-resolution photos cannot show early symptoms of infection.

Use of AI would be an alternative. You create and train a neural network using deep learning algorithms. By feeding the neural network images of infested land during this training, the network learns to recognize samples that indicate infestation. The country you want to study is then fed to it in the form of satellite photos.

As indicated above, remote sensing and geospatial technologies can be used in agriculture to measure crop temperature. When an infestation occurs, plants respond by heating up as they are not receiving enough water or nutrients.

manage irrigation

Geospatial in agriculture easily copes with the difficult task of monitoring vast fields to ensure each crop is getting adequate water.

Images captured by aircraft and satellites with high-resolution cameras enable AI algorithms to determine water stress in any crop and identify visual patterns behind water scarcity.

You can determine how well your current irrigation system is performing by combining these photos with maps of the water supply system.

Prevention of erosion, floods and drought

Agriculture and GIS can work together to prevent, assess, and mitigate the impact of harmful natural events.

Using flood inventory mapping techniques, you can locate regions prone to flooding. You need to gather information about past floods, field studies, and satellite photos. Create a dataset with this data to train a neural network to identify and map flood risks and you have the perfect disaster management tool.

The Universal Soil Loss Equation (USLE) can be used in conjunction with GIS and remote sensing to assess a property’s susceptibility to soil erosion. Perform spectral analysis on satellite photos to validate USLE factors, then confirm these images with field measurements. As a result, you can create a map showing how much the soil has deteriorated throughout the field.

Agricultural drought management techniques can be applied.

Final Thoughts

The number of applications and awareness of GIS has increased significantly in recent years due to advances in digital technologies that use GIS as an essential partner technology for the assessment of plants, soils and their surroundings.

GIS are used at every point along the agricultural value chain. The development of digital farming tools and technologies has increasingly harnessed the power of GIS in new and emerging applications in high-value crop monitoring, yield prediction, precision farming, and supply chain management for both primary products and the use of biomass for energy production.

Precision farming can increase farm productivity and profitability through the use of location and spatial intelligence, and GIS offers a wide range of capabilities and insights, including recent improvements to collect and analyze data in real time. GIS, thanks to its current and future applications, along with legacy and newer partner technologies, is essential to ensure sustainable agricultural productivity.



The views expressed above are the author’s own.