Research advances in computer vision technology are changing traditional agriculture processes and farming. From farming robots to automated plant diagnosis, computer vision and deep learning have fostered many advancements. As the prominence of computer vision and artificial intelligence in agriculture continues to build, Zion market research predicts that these agri-tech improvements will propel agriculture to previously unattainable levels helping preserve the legacy of farming for future generations.
With the growing significance of AI in computer vision-enabled farming, we see state-of-the-art technologies to monitor, analyze, and enhance plant growth and game-changing developments in data-driven predictive decision-making. By integrating advanced technology, farmers dynamically capture soil conditions, measure plant growth in real-time, control pesticide usage, and generally grow healthier crops. Besides boosting production, this smart agriculture technology minimizes production costs. This article further reveals key areas in the evolution of agriculture computer vision applications that benefit farming.
Computer vision and smart agri-tech
Given the ever-increasing global population, challenges related to food production are of serious concern. According to UN reports, the expected population growth by 2050 will require a 70% increase in food production compared to current levels. Thus, it is essential to address the critical issues affecting crop productivity proactively.
Smart agri-tech is a new approach that promises to meet the future demands and necessities of the food supply. However, smart agriculture is still in the prototyping stage in many countries. Elevating the urgency for these countries to incorporate modern agri-tech practices powered by AI, machine learning, computer vision, and deep learning information technology can secure the future of sustainable agriculture and farming.
Computer vision is a machine’s ability to auto-detect, analyze, and understand useful information from a single image or sequence of images. The machine further guides the user in making reliable decisions with the help of extracted data and built-in intelligence. Smart agri-tech offers some of the most significant impact delivering critical information at the right time to farmers. With information technology, smart agriculture has become even more impactful, assisting and guiding farmers in remote areas where reaching experts is difficult or impossible.
Computer vision also helps farmers and agricultural units in adopting precision farming techniques. Precision agriculture focuses on increasing production and efficiency. Soil sampling, geographical information systems, yield mapping, global positioning systems, automated tractor navigation, and robotics are a few practices that distinguish precision farming.
Advancements in computer vision applications for agriculture
Computer vision applications transform agriculture by adopting state-of-the-art techniques at great speed to improve productivity and reduce production time. Below we discuss a few examples of how computer vision transforms modern-day agriculture.
Crop yield quality and quantity enhancement
Computer vision is used to drive the production of high-yield crops rich in minerals, vitamins, and nutrients that promote good health. Computer vision and machine learning algorithms help farmers determine soil richness, natural treatments, and pest controls. Computer vision technology can also identify contaminated food products and defects in crop yield by categorizing them based on color, shape, size, and surface texture.
Detection of plant diseases at the right stage helps farmers adopt preventive measures. Computer vision technology monitors and classifies plant leaves from one crop to another. Image segmentation and image identification techniques identify plant leaf disease utilizing genetic algorithms informing the farmer of conditions that require attention.
Grading and sorting of fruits and vegetables
Deep learning-enabled object detection models trained with computer vision algorithms classify and grade items based on trained parameters. For instance, when a deep learning algorithm is presented with a set of “good” fruits (let’s consider grapes) and another bunch of grapes with defects, the model learns from the data. Ultimately, the model will distinguish between bad and good grapes with a high accuracy rate. Automatic grading and sorting reduce manual efforts, delivery time, and the cost of production.
Advanced computer vision algorithms perform plant phenotyping. In this process, a camera will monitor, for example, 1 to 3 plants in a row at a minimum. The system collects sample images from time to time to analyze and identify plant features like height, width, color, and estimated fruit yield. The plant phenotyping then helps create a better understanding of the functioning of the crops and is often used to calibrate crop models.
Image processing for precision agriculture is advancing indoor farming. Monitoring artificial climate conditions, assessing plant growth, detecting plant diseases, and soil fertility dynamics are a few ways computer vision and machine learning algorithms are automating indoor farming.
Facing rising global populations and limited resources, agri-tech promises to materially boost global food production and global food supplies through investments in smart agriculture technology powered by computer vision, deep learning, and AI for a sustainable farming future. This technology helps monitor, measure, identify, and classify different aspects of farming for higher crop yield and better crop quality.
If you would like to learn more about implementing computer vision in your business, send us your query to email@example.com. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with . develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai