Concurrent Neural Network for Produce Recognition
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Solution Overview
Problem
Existing produce recognition methods are labor-intensive and inefficient, particularly in industries like grocery stores and agriculture, due to the complexity of recognizing various produce categories, organic types, and bag types in images, which can vary significantly in color, texture, and environment.
Innovation Solution
A concurrent neural network model is trained on images of produce to recognize categories, organic types, and bag types simultaneously, using a training set generated by associating images with indicators and preprocessing data to reduce bias and enhance accuracy, allowing for faster and more accurate predictions compared to separate models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate models are used to recognize produce category, organic type, and bag type, then each model can be specialized and trained independently, but the overall system complexity increases and computational resources are consumed more heavily
Solution Approach 1:
The patent combines three separate recognition models (produce category, organic type, and bag type) into a single concurrent neural network model that processes all three recognition tasks simultaneously. This merging approach reduces system complexity and computational resource requirements while maintaining the specialized functionality of each recognition task through dedicated output layers.
Solution Approach 2:
The neural network model is designed with multi-functionality to perform three different recognition tasks (produce category identification, organic type detection, and bag type recognition) using a single unified architecture. The model takes an image as input and simultaneously generates predictions for all three categories through shared feature extraction layers and specialized output layers.
2Reliability
If multiple separate models are deployed for produce recognition, then each model can be optimized for its specific task, but computational power and memory usage increase significantly
Solution Approach 1:
The patent merges three separate recognition models into a single concurrent neural network that processes all three tasks simultaneously. This consolidation reduces the total computational power and memory requirements compared to running three separate models, as the model shares feature extraction layers and computational graph structures across all three recognition tasks.
Solution Approach 2:
The neural network performs all three recognition tasks in a continuous, unified computation process rather than sequentially executing separate models. The concurrent architecture allows the model to extract features once and then branch into multiple recognition pathways, eliminating redundant computations and optimizing energy efficiency.
3Measurement precision
If manual produce recognition is used, then detailed inspection can be performed, but labor intensity increases and processing speed decreases
Solution Approach 1:
The patent replaces manual visual inspection with an automated neural network system that uses deep learning to recognize produce categories, organic types, and bag types. The model processes images through convolutional layers and pooling operations to extract features and generate classifications, eliminating the need for human labor while maintaining high recognition precision through sophisticated feature extraction and classification algorithms.
Data Source
AI summary
In some embodiments, a method can include capturing images of produce. The method can further include generating simulated images of produce based on the images of produce. The method can further include associating each image of produce from the images of produce and each simulated image of produce from the simulated images of produce with a category indicator, an organic type indicator, and a bag type indicator, to generate a training set. The method can further include training a machine leaning model using the training set such that when the machine learning model is executed, the machine learning model receives an image and generates a predicted category indicator of the image, a predicted organic type indicator of the image, and a predicted bag type indicator of the image.