Enhancing Agriculture Operations Through GIS

Owing to the general improvement of technology over the last few decades, Agriculture gained a lot from GIS.              

FREMONT, CA: Agriculture's GIS application is all about evaluating the land, visualizing field data above a map, and putting that data to use. Precision farming, founded on GIS, aids farmers make sensible decisions and taking action to increase each acre's value while reducing environmental impact.

Respecting equipment, agriculture's geospatial technology is contingent on satellites, aircraft, drones, and sensors. These technologies allow the creation of images and their connection to maps and non-visualized data. Consequently, farmers will accept a map with crop location and health status, topography, soil type, and fertilization report, among other things.

Geoinformatics has diverse uses in agriculture. Consider a few of them.

Crop yield forecasting: Accurate yield forecasting can help governments ensure food security and businesses foretelling revenues and budgeting. Recent technological improvements that connect satellites, sensing, big data, and artificial intelligence may allow those forecasts.

Convolutional Neural Networks(ConvNets or CNNs) is one of the most sophisticated techniques in this discipline. A ConvNet is a deep learning algorithm that identifies a crop's productivity. To discover productivity patterns, developers train this system by feeding photos of crops whose yields are earlier known. CNN's accuracy rate is around 82%.

Crop health surveillance: Manually examining crop health across multiple acres is the least efficient method. This is where remote sensing and GIS in agriculture can help.

Satellite photos and input data can be utilized to analyze environmental variables over a field, comprising humidity, air temperature, and surface conditions. Precision farming, contingent on GIS, can improve such an assessor and help farmers decide which crops need further attention.

A more refined system observes crop temperature using image sensors on satellites and air vehicles. When temperatures are greater than normal, this may present the presence of a disease, an infestation, or inadequate irrigation.

Also, neural networks like CNN, Radial Basis Function Network (RBFN), and Perceptron can be utilized to monitor crop health. In addition, algorithms can analyze photos in search of dangerous patterns.