Growing and shifting consumer demands in the agricultural industry requires new inventive solutions.
There is a need to produce more crops despite resource constraints. Also, recent patterns of consumer preferences gravitate towards sustainability, which requires more resources and more advanced solutions.
This clash is not a new phenomenon in agriculture. Recently, the industry has been turning to technology to optimize the crop production cycle. However, some areas of agriculture have become overheated because of low automatization.
Inefficiency leads to a competitive disadvantage and higher operating costs, and we offer our solution to this problem.
The market of artificial intelligence (AI) software has been experiencing steady growth, feeding into the industry’s technological evolution. CVisionLab developed agriculture-related applications expanding on core AI capabilities and enabling robots to either aid a human worker or execute the tasks by themselves. Our solutions reflect the current and emerging trends in the industry and represent the possible applications for these technologies.
The camera used for machine vision, factory automation, print inspection, medical and many other.
Developing software for semi or fully automatic robot manipulation.
Fully automated production cycle with no human involvement.
Require a rise in food production
Drop of field and crop workers since 2002
Raisen cost of employing one worker
According to a new UN DESA report, the world population is expected to reach 9.7 billion in 2050. Subsequently, this increases the need for production efficiency to meet the demand and feed this number of people. It will require an approximately 70% rise in food production.
The supply of field and crop workers has been rapidly declining – resulting in a 20% drop since 2002. The labor shortage reduces crop yields, which amounts to approximately $3 billion of lost annual revenue.
Labor costs account for 40% of total farm business costs. Also, the cost of employing one worker has risen by 89% between 2002 and 2012. This phenomenon can be explained by the combination of the reduced supply of farm labor and the specifics of hiring seasonal workers.
The need to make farming more efficient remains one of the greatest imperatives for automatization. In the context of rapidly growing production demands, labor shortages and rising human labor costs, companies can address all these issues by utilizing AI-powered solutions.
Agricultural Robots can be applied for agricultural production activities, such as achieving a higher crop yield and working continuously without taking pauses or breaks.
Crop and Soil Monitoring utilize vision and machine-learning technology to observe and train in their surroundings. It can be used to process data and ensure a healthy environment.
Predictive Analytics takes the uncertainty out of crop production. Machine-learning models are able to find correlations and other statistics and can be used to keep track of many environmental aspects relating to production.
To help employers gain a clear understanding of the internal processes, CVisionLab’s solution utilized computer vision algorithms.
The method is based on deep neural network capabilities for image pattern recognition. This allowed us to spot workers violating health and industrial safety regulations. However, the scope of application extends to many other areas.
The use of robotics for a vast range of activities from harvesting and picking to pollination and spudding have the potential to become largely automated. These tasks are just a few examples of relevant robotic applications within the industry. Thus, robots can fulfill multiple required functions in an extremely accurate and quick way.
However, the process comes with particular challenges. We can observe these difficulties in different contexts of robotic system applications.
The technology has to be able to visually distinguish multiple factors. To meet this need, our solution has the capabilities to:
Additionally, even after resolving the issues with vision systems, there are other considerations. Robot navigation has to overcome obstacles in its way without damaging the produce. A robotic arm should be firm enough to get a good grasp but also delicate enough to keep the product undamaged.
We approached these challenges with a combination of methods. First, to determine the location and other visual factors, we utilized Neural Network architectures. It ensured the detection and segmentation of different parts of plants. Consequently, a robot gained the capability to differentiate between fruits, leaves, and branches.
Accurate pattern recognition is also ensured by the dataset containing images with different angles and light conditions. It was important to factor in different environments, such as produce growing in the field as well as greenhouses.
To complement the advanced image detection, we added sensing technology based on depth sensors. It addressed the navigation and trajectory generation issue. This addition helps a robot to maneuver in different settings. Also, the advancements in multi-sensor management and fusion assist in the accurate placement of a robotic arm.
Robots and technological improvements replacing human labor is not a novelty. However, the way they are integrated into existing agricultural conditions is getting more advanced and productive. The upcoming years may show an even more extensive array of tasks that AI and robots can complete.
However, it does not mean that it will incur employee layoffs. As organizations face the realities of working in an economic downturn, they are likely to adapt their business models with automation while retaining human workers. Organizations understand that the investment they have made in their existing staff will pay off. Therefore, by applying more automation, they will be able to scale up the use of robots rather than scale down human employees.
The method of solving challenges with particular areas of agriculture shares common elements with other ones. Therefore, our solution can be easily applied to other agricultural operations and outperform human workers just as efficiently. As industry changes occur and new opportunities are identified, this technology will be modeled after them.
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