AgriLens:

AI Vision for Fresh Produce Quality

Real-time computer vision solutions for food producers, distributors, and retailers. Gain instant insights into product quality with AWS-powered analytics.

Transforming the Way the Fresh Produce Industry Sees Quality

In today’s fast-paced fresh produce industry, ensuring consistent product quality is a constant challenge. Traditional inspection methods often rely on manual checks that are time-consuming, subjective, and difficult to scale.

Pointwest’s AgriLens changes the game.

AgriLens is our AI-powered computer vision solution, built using the analytics and AI services of AWS, designed to help fresh produce suppliers, distributors, and retailers gain real-time insights into product quality—straight from images. Whether it’s checking ripeness, detecting defects, or verifying freshness, AgriLens makes quality inspection faster, more accurate, and scalable.

How AgriLens Works

Image Capture

A simple photo from a mobile device or camera feed.

AI-Powered Analysis

Machine learning models trained to detect ripeness, quality, or defects specific to the product.

Instant Insights

Results delivered in seconds, enabling faster decisions on sorting, packaging, or distribution.

Why AgriLens?

Consistency at Scale

Remove the guesswork from manual inspections and achieve uniform quality standards across operations.

Cost Efficiency

Reduce waste and improve supply chain efficiency by ensuring products are distributed at the right stage of maturity.

Flexibility Across Products

From pineapples to tomatoes, melons to mangoes—AgriLens adapts to different crops and food products.

Seamless Integration

Deployable via mobile app, APIs, or embedded into existing quality control workflows.

Who AgriLens is For

Large-Scale Agricultural Producers

Enterprises managing high-volume crop production that require consistent and objective quality assessment across farms.

Export-Oriented Farms with Consistent Quality Standards

Producers supplying international markets where strict grading criteria and compliance requirements must be met.

Agricultural Processors Requiring Yield Predictability

Processing facilities that depend on reliable maturity classification to optimize throughput, reduce waste, and improve production planning.

Customer Engagement and Delivery Framework

AgriLens is delivered through a structured, enterprise-ready engagement approach.

Proven in Action

In a recent proof of concept with a global food producer, AgriLens successfully classified the internal ripeness of fruits using nothing more than cross-section images. The solution improved quality inspection accuracy and demonstrated the potential to revolutionize how food companies manage product consistency.

The Challenge

A major Philippine fruit producer relied on manual inspections to assess ripeness of their fruit produce. The process was subjective, time-consuming, and inconsistent, making it difficult to ensure quality at scale.

The Solution

Using AgriLens, Pointwest developed an AI-powered computer vision model to classify fruit maturity from cross-section images. The solution combined:

Image preprocessing

To handle lighting and orientation variations

Object detection and cropping

To avoid false positives from non-fruit images.

Deep learning models

For higher accuracy, supported by data augmentation to simulate real-world conditions.

Core AWS Services

AgriLens is built entirely on AWS cloud infrastructure, ensuring enterprise-grade scalability, security, and performance. The following core AWS services were used:

Amazon Sagemaker

Enables the building, training, tuning, and deployment of computer vision models at scale, allowing AgriLens to accurately detect and classify fresh produce quality while reducing infrastructure management overhead and accelerating model iteration cycles.

Amazon S3

Provides highly durable and secure object storage for raw images, labeled datasets, and trained model artifacts, ensuring scalable data management while supporting both model training pipelines and real-time inference workflows.

Amazon API Gateway

Securely exposes model inference endpoints to mobile devices and web applications, enabling field users to submit produce images and receive near real-time quality assessments through a managed, scalable API layer.

AWS Lambda

Runs serverless backend logic that orchestrates image processing, triggers model inference, and integrates results with downstream systems, allowing AgriLens to operate efficiently without managing dedicated servers.

The Benefits

Higher accuracy

In ripeness classification

Fewer false positives

Even with distracting objects

Scalable framework

Adaptable to other fruits and food products

Foundation for mobile deployment

To enable quick, offline inspections