How Agriculture is Benefited by Computer Vision

Computer vision is a field of study that seeks to develop techniques to help computers see and understand the content of digital images such as photographs and videos. Our work in Computer Vision & Machine Learning powers innovation in areas of various sectors through Accurate & high quality labeled Data from our Professional & well-trained annotators and so Data annotation and Data Labeling comes into the picture. Computer Vision Use-Cases: Image Classification, Object Detection, Video Analytics, Image Segmentation, Facial Recognition, Emotion Analysis. 

Computer vision technology is very highly significant and dynamic and it’s been selected by many industries in many different ways. The difference is some use cases happen behind the more visible or some are not. One of the significant use cases of computer vision is the Agriculture sector 

Presently, many startups in the agriculture sector are adapting new and effective AI-enabled approaches to increase the efficiency of agricultural production, and there many use cases under which the agriculture sector is benefited through AI. The major factors such as climate change, food security, advanced agricultural techniques, Checking and monitoring soil to improve crop conditions. 

Soil Monitoring through Computer Vision-Enabled Farming

One of the most important AI applications used in the agriculture sector is  Monitoring the farms, farmers are using Computer Vision and 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 to recognize many crop problems and their areas and 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. 

AI & Computer Vision helping in 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 such as weather changes and their conditions reciprocated in crops & land for the particular year.  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.

Helping in 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.

Unlock advanced business opportunities with Data Labeling & Data Annotation  for COMPUTER VISION 

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