Sustainability through artificial intelligence
Farming for the future today
THE SMART AGRICULTURE SOLUTION
IMAGINE HAVING THE ABILITY TO HIRE AN ENTIRE TEAM OF EXPERT AGRONOMISTS TO WORK AROUND THE CLOCK – MONITORING AND CONTROLLING EVERY ASPECT OF YOUR GROW, ANTICIPATING DISEASE AND MAKING SUGGESTIONS TO IMPROVE THE OUTCOME OF EACH CROP
Hiring this kind of team would be costly and impractical for most facilities. Fortunately, NGA discovered a way to use advanced AI and machine learning to build Agronomist AI – a revolutionary virtual agronomist who can do what no human can – process millions of points of data to predict the future.
But developing an AI solution is not about replacing growers; it’s about augmenting their jobs by allowing them to be in two places at once, work proactively instead of reactively to solve plant growth issues in real time, and redirect the way they use their time by reducing the number of hours dedicated toward scouting crops and walking t through greenhouses to check environmental conditions. Instead, growers can check crop conditions over coffee remotely, and flag problems, create task lists, and assign tasks to staff members before they walk into work in the morning.
Hydroponic system is a new cropping innovation in agriculture. In biological studies, plant growth and health assessments are still evaluated manually by human observations, which are time consuming and destructive. Because of this there is an increasing demand for objectivity and efficiency. Thus, automatic image analysis technique has become a useful tool in biological researches. Image analysis method is a non-invasive and non-destructive sensing system. It can be used to extract and quantify different kinds of information like size, shape, colour, moisture and growth rate of a specific plant.
Using precision imagery plus AI to revolutionize sustainable Farming
“INSIGHT TO GROW BEGINS TO CAPTURE GROWING DATA FROM THE MOMENT THE SYSTEM IS SWITCHED ON IN THE GREENHOUSE”
The system uses imagery and environmental sensors to monitor plant growing conditions in real time. Growers can view a specific plant, from anywhere through software accessible on handheld devices. They set the environmental and growing parameters and are able to set up alarms if those conditions change. With access to recorded data, growers can also “go back in time,” to view and compare plant growth and conditions from previous days, weeks, months, and even years, side by side with current crop conditions.
With the ability to record historical data, artificial intelligence will provide grower operations with a way to develop tribal knowledge in growing practices, to automate the production process in a way that is consistent and repeatable, no matter who comes or goes from the operation.
NGA gives growers the data they need for proactive management based on precise knowledge, and ideally will help growers maximize product yields and quality, reduce operational costs and waste, and confidently predict ready dates.
With the right technology, thoughtfully applied to give them better computer driven visibility, greenhouse operators can be as precise, proactive, and predictable as modern manufacturing operations.”
GPS based crop and zone marking sensor and manual data input external enrichment and training
GPS CROP ZONE MARKING
Zone markingallows you to easily capture the boundry corners of the crop area and define what type of crop will be grown
AUTOMATIC SENSOR BASED INPUTS
In the event that an exception occurs, users linked to the zone in question will be immediately alerted to problem that requires treatment and advises what actions to take
MANUAL DATA CAPTURE
The mobile app allows the user to capture additional events that will enrich the data for monitoring, analysis and decision support
EXTERNAL DATA ENRICHMENT
Using machine learning techniques, this information is used in conjunction with the metrics acquired from IOT sensors and the data captured manually to predict growth risks and productivity
Early warning for anomaly and disease detection
AI DISEASE DETECTION
We use image analysis techniques involving seven basic steps like image recording, pre-processing, image segmentation, detection, extraction, classification and finally validation. The identification is based on colour index and digital image matching. The success degree of the research is up to 95%
HYPERSPECTRAL SAMPLING FOR EARLY DETECTION OF DISEASES
Using a combination of hyperspectral imagery and environmental sensors, the system will monitor plant growing conditions in real time and alert mobile users to any exceptions occurring. Users set the environmental and growing parameters and are able to set up alarms if those conditions change.
If a transaction or reading meets the criteria established for a strategy or scenario, it is displayed visually to analyze and if required enhancements can be added. Plant and Tunnel statistics relating to the scenario setup are also calculated each time a scenario is run or new sensor reading is obtained. The Central Intelligence Centre will be hosted on the cloud and can be accessed remotely from any device, and will provide the user with useful information.
NGA created a proprietary algorithm to assign an overall health score to a Plant Zone based on various factors. The health score is used within our algorithms to assist with forecasting and to identify trouble areas.
ENHANCING TRADITIONAL FARMING METHODS WITH AI REINFORCEMENT LEARNING
The focus of I2g is to gather vast amount of information regarding plant health over a period of time. Using the data gathered, we develop deep learning algorithms in order to determine the optimal levels of various measurements that will enhance quality and productivity and predict the impact certain events will have on the overall production.
– Plant and Tunnel behavior analysis
– Monitors an Unlimited number of scenarios
– Specific to plant type grown and products
– No Fixed Rule
– Auto Scenario Creation
All our systems are tested within our own tunnel and most of our produce is donated to local organizations .