To star AI in agriculture you have multiple options to integrate this technology into farming field. Actually, to start AI in agriculture you have to develop an AI model like application, system, robot or machines that can leverage the power this advance technology and boost the productivity.
AI in agriculture is playing a big role in analyzing the soil before sowing the seeds, monitoring the crop health or maturity level, controls the pesticides and forecast the weather conditions and predict the other natural calamities to minimize the loses from such threats.
AI Model for Agriculture
To start the AI in agriculture you can develop AI-based model like autonomous tractors, robots, drones and weed controlling machines or other similar devices. All such devices will help in what discussed in previous paragraph to improve the agricultural productivity at large scale.
Here you have two options to implement the AI in agriculture — either uses the already developed AI-based models like robots or drones to analyze the crop health or you can develop such machines with the help of machine learning engineers and data scientists.
How to Develop AI model for Agriculture?
If you are not interest to use the AI model already available in the market you can develop your own model using the machine learning and deep learning techniques. Actually, both robots and drones works on computer vision based technology, and machine learning algorithms are also trained with huge amount of training data available from agricultural fields.
How to Get Training Data for AI in Agriculture?
To get the training data sets for AI in agriculture you can get in touch with companies like Anolytics providing the high-quality data sets to develop such models. It is providing the annotated data sets of crops from different view to train the visual perception based model can recognize the real crops, unwanted crops, insect and help to monitor the health conditions.
This company providing the image annotation service to annotate the different types of images precisely helping the AI developers to create such models for agriculture sector. Using the right quality of agriculture training data you can start and use the AI in this field and improve the productivity with higher crop yield with better efficiency at less cost.