Computer vision is a subfield of artificial intelligence rapidly changing the world around us. Its main goal is to process, analyze and interpret visual data just like the human brain can.
While a decade ago it offered only limited functionality, due to constantly evolving AI technological advancements and the amount of data we generate daily its capabilities have increased exponentially.
From 50 percent accuracy, nowadays we are reaching an outstanding 99 percent, making it more precise than human vision reacting to quick visual inputs.
Computer vision technologies have been implemented across various fields. They range from visual systems developed to inspect faulty products in the manufacturing industry, to research about creating an intelligent machine that would be able to comprehend the world around them. Some of the best-known computer vision applications that are already making a significant impact on humanity include:
Needless to say, computer vision innovations are revolutionizing the agricultural industry too. An ever-growing human population increases the demand for produce, requiring greater efficiency in the fields of crops.
From lowering the cost of production to improving productivity, artificial intelligence-powered technologies are able to perform highly sophisticated tasks that alternatively only humans can complete. The agricultural industry profits from a wide variety of applications, including sowing, harvesting, weather condition and soil analysis, weeding, crop health detection and monitoring, etc.
Even though our livelihood depends on it, agriculture continues to be one of the most underestimated low-tech sectors. By eliminating outdated machinery and implementing innovative artificial intelligence technologies we are on track to changing that. With integrated smart farming solutions agriculture would boost the economy from the global market perspective, allowing countries to generate more produce at a lower cost. Some of the computer vision applications in agriculture that have already been successfully implemented across the world include:
Several months ago we started working with one of our clients in Argentina and Brazil on an artificial intelligence solution for the agricultural industry. It has been developed for yield analysis conducted from aerial view images of the fields obtained by computer vision-enabled drones.
To make the image annotation process easier, images acquired from the drones are modified by removing the soil around them. Moreover, the images are captured from the top, so the position of the crop stem does not match the position of the top of the crop on the sides of the image, therefore, we perform perspective correction calculations to take into account these discrepancies.
The final solution is composed of three parts:
After the thorough detection processes, we calculate the number of plants in each row of crops, plant population per hectare, the total length of spacings between crops, the coefficient of variation of spacing lengths, and other relevant statistics for further analysis. These findings provide opportunities for the following applications:
The solution is already proving its use within the agricultural industry, helping bring about a positive change in farmers’ lives. Knowing the amount of the produce is crucial for cash-flow budgeting, delivery estimates, planning harvest equipment, storage requirements, and crop insurance purposes.
According to the report about the future of farming technology, it is estimated that due to the unstoppably growing population, by 2050 agricultural industry will need to produce 70 percent more food. If we keep the agricultural technology sector as it is now, we will face serious food shortages affecting millions of people around the world.
However, with the use of artificial intelligence solutions, autonomous drones, and robots, we can improve efficiency, reduce the workflow for the farmers in the fields and allow them to concentrate on more pressing matters. By investing in the development of computer vision applications for the agriculture sector, we can expect to see a return on this investment through the efficient production of goods on a mass scale. It is also reasonable to believe that as these technologies become more commonplace, they will be more readily available and filter down into horticulture and be distributed by garden machinery and maintenance suppliers like Horace Fuller.
SentiSight.ai is an online-based image recognition platform offering extensive image labeling and model training tools. It was designed with two main goals in mind: to make image recognition tasks as easy as possible and to offer a powerful and user-friendly image annotation tool for its users.
Just like our client in South America, everyone is able to upload their images to the platform, label them, and use the data to train a deep convolutional network model. Although the process sounds complicated, we have tried our best to make it as simple as possible.
To get started, check our blog post library about how to choose the right image labeling tool for the job and how to train an object detection model with your data. The SentiSight.ai platform is here to help you develop the solution of your dreams, whether that be within the agricultural industry or others like retail.