glosys Agriculture AI Overview
glosys Agriculture AI, One of the components of glosys AI Platform, provides AI & Cloud Software Suite and SaaS Applications and Services for AI and Analytics Team to focus on Agriculture Data Management, Annotation Automation, Model Management for Agriculture, MLOps Automation and Agriculture data security in the digital world
Features We offer
Agriculture Data Management
glosys Agriculture AI manages Agriculture data sets in terms of data extraction, transformation, loading and processing effectively
Agriculture Annotation Automation
glosys Agriculture AI focuses on auto-annotating, Agriculture image and video annotations, interpolate and fine tune the performance of the annotator for Agriculture data
Agriculture Models Management
glosys Agriculture AI leverages construction of Agriculture ML Pipelines, Auto-ML to develop production-ready Agriculture AI, Model Workflows, Models Hub and Model Metrics for Agriculture data
Agriculture MLOps Automation
glosys Agriculture AI harnesses the power of construction of Agriculture CI/CD AI Pipelines using built-in neural networks, python SDK, webhooks and advanced orchestration
Agriculture Data Security
glosys Agriculture AI protects and secures image and video data using security features such as roles management, profiles and permissions management, access control and field level security
Model Types glosys Agriculture AI leverages
Object Detection & Localization
Detect and locate the presence of multiple objects within an image, drawing bounding boxes around them to indicate their position and size
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Object Classification
Identify and assign a label or multiple labels to images based on the presence or absence of specific features or patterns within the image
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Captioning & Action Recognition
Automated process of generating textual description of an image, identify and classify human actions or movements within a video using action recognition
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Semantic Segmentation
Divide an image into distinct regions or segments, and assign labels representing the category of objects or features they belong to, to each individual pixel
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Instance Segmentation
Detect and delineate individual object instances within an image, and assign a unique label to each pixel that belongs to that instance
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Object Tracking
Follow or track the movement of one or more objects within a video sequence by detecting and matching features across frames.
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Usecases for Agriculture
Monitor Crop Health
Classify between different crop species and weeds
Analyze soil moisture, temperature, humidity, and nutrient levels to assess the overall health of the crop and provide insights into irrigation and fertilization needs.
Development of Agriculture Drones with Maximum Weight, Operating Speed, Operating Altitude, Endurance, Payload, No. of Motors, No. of Propellers, Controller, Nozzle Type, No. of Nozzles, Spray width, Acres Coverage, Power System, Type of Fuel, Fuel Tank Capacity and Back up
Forecasting Pest Infestations
Certain pests thrive under specific weather conditions. Monitoring temperature, humidity, rainfall, and other weather parameters helps predict the likelihood of pest outbreaks. Weather stations and online weather services can provide valuable data.
Analyze historical data, weather patterns, and other relevant information to predict pest outbreaks.
Identify the presence of pests and provide early warnings to farmers, allowing for targeted pest management strategies.
Identify Crop Diseases
Visual observation of symptoms on plants, such as wilting, discoloration, spots, lesions, and deformities.
Inspecting crops for signs of diseases with the help of drones equipped with cameras for aerial monitoring.
Plant pathologists analyze samples of infected plants in laboratories using microscopic examination, DNA testing, and other molecular methods, to identify pathogens.
Mobile app uses ML algorithms to identify potential diseases of affected crops
Weed Detection and Management
ML models differentiate between crops and weeds in images, enabling automated weed detection. This information can be used to implement targeted weed management strategies, reducing the need for herbicides.
Update Field Data
Seasonwise Field Data Updation by covering large areas daily in terms of Unmanned Aerial Vehicle
Hyperspectral and multispectral sensors detect subtle changes in plant health that may indicate the presence of diseases.
Satellite and drone-based remote sensing technologies monitor large agricultural areas to detect changes in vegetation health, helping to identify potential disease outbreaks.
Improve Crop Yields
Predict crop yields based on weather conditions, soil health, and historical yield data to make informed decisions regarding planting, harvesting, and resource allocation.
Meet Your AI for Agriculture Objectives & Needs