Agriculture has benefited from GIS due to the general improvement of technology over the last few decades
FREMONT, CA: Agriculture's usage of GIS is all about evaluating the land, visualizing field data on a map, and putting that data to use. Precision farming, which is based on GIS, helps farmers to make informed decisions and take action to maximize the value of each acre while minimizing environmental impact.
In terms of equipment, agriculture's geospatial technology is based on satellites, aircraft, drones, and sensors. These technologies enable the creation of images and their connection to maps and non-visualized data. As a result, farmers will receive a map with crop location and health status, topography, soil type, and fertilization information, among other things.
Geoinformatics has a variety of uses in agriculture. Consider a few of them.
Crop yield forecasting: Accurate yield forecasting can assist governments in ensuring food security and businesses in forecasting revenues and budgeting. Recent advancements in technologies that connect satellites, sensing, big data, and artificial intelligence may enable those forecasts.
Convolutional Neural Networks is one of the most sophisticated techniques in this discipline (ConvNets or CNNs). A ConvNet is a deep learning algorithm trained to recognize a crop's productivity. To uncover productivity patterns, developers train this system by feeding photos of crops whose yields are previously known. CNN's accuracy rate is approximately 82 percent.
Crop health surveillance: Manually inspecting crop health across numerous acres is the least efficient method. This is where remote sensing in conjunction with GIS in agriculture can help.
Satellite photos and input data can be used in conjunction to analyze environmental variables over a field, including humidity, air temperature, and surface conditions. Precision farming, which is based on GIS, can enhance such an assessor and assist farmers in determining which crops require further attention.
A more sophisticated system monitors the temperature of crops using image sensors on satellites and air vehicles. When temperatures are higher than normal, this may suggest the presence of a disease, an infestation, or insufficient irrigation.
Additionally, neural networks such as CNN, Radial Basis Function Network (RBFN), and Perceptron can be used to monitor crop health. Algorithms are capable of analyzing photos in search of hazardous patterns.