Artificial intelligence is helping many industries to increase their efficiency and growth. Basically due to increasing competition, every industry & brand needs to overcome the traditional challenges & have a modern approach.
The modern approach through AI & Data Annotation and Data Labeling helps to reduce costs and attract new customers. Similarly, AI in agriculture is helping
To grow in the most efficient and effective manner.
How AI helps Agriculture Industry
Agriculture is one of the most popular and significant sectors for Computer vision. Applications of Computer Vision for assessing the Quality of Agricultural-food Products.
Specifically, computer vision systems are feasible for classifying food products into specific grades, detecting all defects. It has become an important part of accurate yield mapping, yield estimation, disease detection, crop management, and harvesting, soil analysis technologies, and much more.
Applications of AI in agriculture
- Predicting & Forecasting
Predictive analytics is one of the very helpful AI applications to power the agriculture sector. Machine learning models are being developed to track, estimate, and predict various environmental impacts on crop yields. Computer vision technologies to provide data like analysis of crops, weather, and economic conditions to make the most of the yield for farmers.
Tracking and forecasting help the farmers to remain updated with all-weather conditions data so that farmers can work accordingly. The analysis of the data generated helps the farmer to take the precaution by understanding and learning with AI. By implementing such practice helps to make a smart decision on time and increase profits. AI provides farmers to analyze data like Weather conditions such as temperature, rain, wind speed and direction, and solar radiation to prevent losses by taking many useful measures.
- Robots – Researchers & many companies are developing efficient robots to do and manage essential agricultural activities. They help to perform important tasks i.e. harvesting crops in larger quantities and saving time and energy.
- Soil Monitoring – One of the most important AI applications used in the agriculture sector is
Monitoring the farms, farmers are using Computer Vision & deep learning algorithms to capture data from drones flying over their fields to check crops and soil. As through drones AI-powered cameras capture pictures of the entire farm and evaluate the images in near-real-time. It helps to recognize many crop problems and their areas. After seeing reports improvements and solutions can be taken care of.
Thus, computer vision abled farming helps to improve the soil and crop conditions faster as drones are able to capture more land in much less time than humans. Software-based technology to monitor crop and soil health and without any doubts, these AI-enabled applications are of great help to the agriculture sector to recognize soil defects, plant pests, diseases, and taking out solutions accordingly.
- Weed Detection
AI in agriculture is helping with Weed Detection. Weeds are one of the most common threats to crop and it’s difficult to detect and so Computer vision helps to detect at quite affordable prices and with no environmental issues and side effects.
AI is able to benefit the agriculture sector through
- Machine Learning
- Computer Vision
- Predictive Analytics
Benefits of AI in Agriculture
1) AI provides forecasting that helps farmers to plan future tasks accordingly.
2) AI provides help to sell essential crops.
3) AI helps to detect weed
4) The growth in Artificial Intelligence technology has provided agro-based businesses to work efficiently.
5) AI helps the agriculture sector to solve many challenges of farmers.
6) AI-enabled applications are of great help to the agriculture sector to recognize soil defects, plant pests, diseases, and taking out solutions accordingly.
7) Tracking & forecasting help the farmers to remain updated with all-weather conditions data so as to take actions and reduce losses in the near future.
Did You Know?
The overall AI in the agriculture market is projected to grow from an estimated USD 1.0 billion in 2020 to USD 4.0 billion by 2026, at a CAGR of 25.5% between 2020 and 2026.
We extract meaningful data from real-world images & videos. Our trained teams can help meet requirements for Bounding Box Annotation, Polygon Annotation, Keypoint & Skeletal Annotation, Semantic segmentation, and Geospatial imaging. We are here to Scale your machine learning program quickly and improve user experiences with high-quality, human-annotated data. We understand the challenges in deep learning, images, and video processing and are equipped with the skills to deliver the best solution.
Appoint us for Data annotation services and high-quality, safe, secure Fast project delivery.