Computer Vision is maintaining its position in every sector with the advancement of technology, including banking, finance, social media, vehicles automation, and becoming more popular in Agriculture. Computer vision has massive potential for the overall functioning of the agricultural sector. It boosts productivity, reducing production costs via intelligent automation. Let’s explore how computer vision is impacting the agricultural industry.
What is Computer Vision?
A branch of artificial intelligence that allows the machine to interpret and visualize data like a human. For example, Computer Vision is used to analyze multiple visuals from images, real-time videos, track, annotate, define, predict and assess specific objects within those visuals. With the incorporation of deep learning, the Computer Vision technique targets the advanced training of AI models. For example, when a human looks at imagery or a scenario, he can note all visible objects with their location, movement, and description. Depending on pattern recognition and existing datasets, Computer vision technology can train the artificial intelligence models to incorporate all these features.
Computer vision techniques use a variety of cameras, including multispectral, infrared, 3-D, thermal cameras, and many more, to provide vision to AI machines. For example, In a fruit detection method, color cameras are used to identify fruit from trees, and for automatic harvesting, stereo cameras are used to detect the relative position of the fruit.
Computer Vision in Agriculture
Incorporating artificial intelligence (AI) and computer vision in the agricultural sector acts as the game-changer by providing more accurate, efficient, automated processes within the industry. Computer vision significantly contributes to agricultural areas like harvesting, planting, plant health detection, monitoring, weeding, and advanced analysis of weather conditions.
The complete food supply chain is powered by various innovative farming use cases that facilitate real-time operational decision-making, provide valuable insights about the entire farming process, and enhance farming practices by introducing on-field intelligent sensors and devices.
Applications of Computer Vision in Agriculture
1. Computer Vision Systems in Drone-Based Crop Monitoring
In precision agriculture and farming, drone and aerial imaging technology have gained massive popularity due to its autonomous flying capabilities and have become the most significant element for crop monitoring. With the help of inbuilt cameras, drones can capture a vast volume of data covering a considerable distance. The captured footage is used to train the computer vision algorithms, which help detect soil conditions, assess crop health information depending on Geo-sensing, and analyze the aerial view of the overall agricultural land. Furthermore, the image annotation service labels these images and makes them training-ready. Finally, this well-trained and efficient dataset is fed into the AI models to recognize objects and crop conditions in the drone footage by performing semantic segmentation.
2. Computer Vision Systems in Harvesting
Computer Vision and deep learning algorithms are used to create highly specialized robots that perform autonomous harvesting of fruits and vegetables. Computer Vision enables robots to identify the fruits and ensures proper grasping. A significant acceleration in process time and reduction of manual labor are the two best benefits of using Computer Vision-based harvesting. In addition, computer vision-based harvesting enables the three most essential functions: fruit detection, location detection, and the physical harvesting equipment to grasp and detach fruit from the plant.
3. Yield Estimation With Fruit or Vegetable Counting
Yield estimation is an essential preharvest practice among various large-scale farming companies. It helps in decision-making for allocating necessary logistics like labor force, transportation, supplies, plant disease prevention, and optimization of cultivation practices. Therefore, an accurate yield prediction is required to avoid crop and money wastage. Computer vision approaches facilitate accurate yield prediction by automatic detection and counting of fruits, flowers, and vegetables on images.
4. Computer Vision Systems in Grading and Sorting
Traditional grading and sorting are fully human-dependent and require a large workforce for larger farms. In addition, the process is highly labor-intensive and takes up significant time to execute for greater quantities of harvested produce. Manual operation can work for small farms but not for manufacturers who produce hundreds of thousands of fruits daily. Computer Vision grading and sorting techniques use intelligent scanners equipped with image classification technology to assess various grading features like color, size, ripeness, and defects to ensure good quality harvesting. Furthermore, it helps recognize crop requirements like antibiotic doses and extra water.
To wrap up
Computer Vision plays a vital role in agricultural activities like grading and harvesting. It continuously improves to facilitate intelligent farming and enhances agriculture by cutting down human labor, providing highly accurate and efficient techniques for recognizing crop requirements, and increasing operational speed.
How Indika Can Help
At Indika, we provide exceptional high-quality data annotation services for computer vision. Our expert annotators label images dataset with precision and accuracy for AI and Machine learning models that facilitate the building of Agricultural technology. Furthermore, our dedicated and responsible team of experts extract intelligence from images enabling precision agricultural and innovative farming technologies to help farmers predict yield, reduce water usage, protect crops and automate farm operations.
For more information about our data annotation service, contact us today. We are flexible and can support any size project to fit your budget. So don't wait, call us now. Our experts are ready to help you!