Integrating AI ( Artificial Intelligence) into various fields has undoubtedly made life easier for humans. Agriculture, being a crucial sector, can benefit a lot from innovative technologies like AI. It can help enhance output, reduce wastage, and improve productivity.
And our experts at Opporture- a leading AI company in the USA, can be reliable partners to businesses in the Agriculture sector looking to improve their operations.
Advanced agricultural machinery, accurate annotation tools, and data enrichment specialists often work with GIS and geographical data to improve the efficiency of farming operations. Computer vision-based crop production & monitoring systems are examples of how AI simplifies agriculture.
Harvesting, ripping, health monitoring, and increasing crop productivity have significantly benefited from the usage of AI. Smart AI robots use computer vision technology to train AI models, which are then fed annotated data and subjected to machine learning algorithms.
Data Annotation in Agriculture
Farmers tend to use their personal expertise to check yields, spot diseases, and anticipate natural disasters. However, advancements in AI can help them use technology for the same purpose. Modern sensors, cameras, and software make it possible for computer vision to automate processes and improve transparency in agriculture. However, these technologies won’t be helpful until training datasets for agricultural usage are prepared.
Image annotation is a crucial part of this process. It includes labeling an image in a machine-readable way by pointing out specific features, outlining entities, and providing various keywords. Data annotation helps automate and improve tasks like fructification detection, livestock inventory, weed detection, soil monitoring, and crop health monitoring.
Annotating images is essential because it helps to produce datasets for use by computer vision models in the real world. Annotating and tagging photos with relevant labels and keywords facilitates further categorization.
Data Annotation Types Used for Computer Vision in Agriculture
- Crop detection by bounding boxes– Annotating with bounding boxes can streamline the crop identification process. It helps develop perceptual models to differentiate weeds and crops and eradicate unwanted grass.
- Size/shape detection of produce by key points– The size and shape of vegetables as well as fruit, can be assessed easily with the use of critical points. It will also have accurate indicators of ripening and fructification.
- Crop inspection using semantic segmentation – This computer vision technique helps with crop inspection by solving several segmentation and classification problems. This includes semantic image and video classification of agricultural datasets and crop detection methods.
- Livestock management with polygons & bounding boxes– Animal husbandry is an integral part of agriculture and can be managed optimally by AI-enabled devices. This involves the use of bounding boxes and polygons. Annotating the animals with polygons and bounding boxes aids in accurate identification.
- Classification of crop lanes with polylines – Since polyline annotation can categorize objects, it can aid in the creation of reliable models for computer vision in the agronomy industry. Tasks such as disease detection, harvest prediction, and irrigation pattern analysis can be carried out easily.
- Tracking crop health using GIS- GIS aids artificial intelligence (AI)-powered farming drones in monitoring crop health. It improves agricultural output and makes it possible to track the state of farm machinery.
Application of AI in Farming
Artificial intelligence (AI) is ushering in a new era of development in the agriculture industry. By using computer vision tech for crop & soil monitoring, disease detection, and predictive analytics, AI is all set to create seamlessness in many processes. Let’s now examine the most exciting AI innovations currently reshaping the agricultural industry.
- Soil and crop analysis: Micro- and macronutrients play a crucial role in ensuring crop vitality and yield. UAVs, or unmanned aerial vehicles, are now routinely used to take aerial photographs, which are then used to educate computer vision models for intelligent crop and soil condition monitoring.
- Pest and disease detection in plants: Plant diseases and pests can be automatically detected using deep learning-based image recognition technology. To construct models that can “keep an eye” on plant health, we use image detection, classification & segmentation techniques.
- Livestock health tracking: Animals are also a crucial part of our agricultural systems, and they require a higher degree of monitoring for their health than plants do. Computer vision can be used for animal counting, disease detection, spotting abnormal behavior, and monitoring important events like births. Farmers can be updated on the health of their livestock using information gathered by Unmanned Aerial Vehicles (UAVs).
- Intelligent spraying: AI sensors can quickly locate problem areas and eliminate weeds. Once these spots are located, herbicides can be sprayed accurately, preventing wastage of time and production. Saving money and improving crop quality are two main benefits of using AI sprayers.
- Auto-weeding: Intelligent sprayers are not the only AI involved in weed control. Other computer vision robotic systems are also taking a more direct approach by destroying unwanted weeds.
- Aerial imaging & surveys: Surveying land and monitoring crops and livestock aerially is another great use of computer vision. AI can analyze drone and satellite imagery to monitor crops and livestock. The accuracy and effectiveness of pesticide spraying can also be improved with the help of aerial imagery.
- Produce sorting & grading: After harvest season is over, AI computer vision can be helpful to farmers by assisting with produce grading and sorting. Computer vision can automate and optimize the grading & sorting of produce by analyzing their size, shape, color, and volume. This process has accuracy rates and processing times that far exceed those of even the most skilled human worker.
- Multipurpose agricultural robotics: Nowadays, various AI companies are developing robots for use in agriculture. The quality of crops, the presence of weeds, and the speed with which they can be harvested are all areas in which AI robots have been trained to work faster than human workers.
Also Read: The Importance of Training Data for Autonomous Vehicles
Agricultural Industry- Future of Data Annotation and Artificial Intelligence
Robots, aided by machine algorithms, can perform various agricultural tasks, including planting seeds, removing weeds, tracking productivity growth, sorting, picking fruits and vegetables, packaging, and grading.
Data-driven farming procedures have increased ease and convenience by automating the various steps involved in a farming process. In the future, when more agricultural devices are linked to base datasets, fully autonomous machinery, and maximized harvests will be within reach. Robotics can be easily combined with efficiently labeled data.
As agriculture evolves into a data-driven sector, we can gain more insights into the ways in which we can farm the food we consume. Hence, it is undeniable that with all of these fantastic technology solutions at our disposal, AI will indeed have an essential role in assisting us in satisfying the rising demand for food while reducing the associated environmental impacts.
Closing note
AI’s popularity is rising steadily across many industries, including agriculture, where it can be used for everything from crop forecasting to developing highly competent agricultural robots. Yet, we have to see and experience the exact full potential of AI. AI will soon assist farmers in becoming agricultural technologists by enabling them to utilize data to maximize yields on a row-by-row basis.
Artificial intelligence (AI) in agriculture is helping farmers automate tasks and adopt precision farming techniques. Though human involvement in the training data creation and validation process is crucial, workflow automation can boost efficiency without compromising quality. Our professionals at Opporture can help your agribusiness take the next step toward using AI & data annotation. Get in touch with us today.
