Crop Image Embedding Trajectories for Growth Deviation Detection
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Solution Overview
Problem
Farmers face challenges in efficiently collecting and processing large amounts of crop growth information to track and characterize crop growth, making it difficult to predict yields and manage crop health, leading to potential losses and inefficiencies in labor and resource allocation.
Innovation Solution
Implementing a system that uses machine learning models, particularly convolutional neural networks (CNN) and recurrent neural networks (RNN), to process temporal sequences of crop images, generating semantically-rich image embeddings and crop trajectories, which are visualized using techniques like t-distributed stochastic neighbor embedding (t-SNE) to facilitate quick evaluation and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If farmers collect and process large amounts of crop growth information manually, then they can track crop growth, but it becomes overwhelming and time-consuming to sort through the information
Solution Approach 1:
The patent replaces manual mechanical sorting and analysis of crop images with automated machine learning models. Convolutional neural networks process temporal sequences of crop images to extract growth patterns, automatically identifying deviations without human intervention. This substitution eliminates the time-consuming manual sorting process while preserving all crop growth information.
Solution Approach 2:
The system creates simplified representations (embeddings) of complex crop images that capture essential growth characteristics. These embeddings serve as compressed copies that retain meaningful information while being much easier to process and compare, enabling efficient tracking without losing critical growth data.
2Measurement precision
If farmers use manual methods to evaluate crop growth, then they can assess current stages, but it is difficult to identify deviations and predict yields accurately
Solution Approach 1:
The patent replaces subjective manual evaluation with objective machine learning-based assessment. The models automatically detect deviations from expected growth trajectories by comparing actual crop images against learned patterns, providing precise measurements of growth status, disease presence, and yield predictions without requiring complex manual analysis systems.
Solution Approach 2:
The system introduces machine learning models as intermediaries between raw crop images and farmer decision-making. These models translate complex visual data into actionable insights about crop health, growth stage, and potential issues, simplifying the evaluation process while improving accuracy.
3Reliability
If farmers implement comprehensive crop monitoring, then they can detect issues early, but the cost and complexity of data collection increases
Solution Approach 1:
The patent implements a universal machine learning framework that handles multiple crop types, growth stages, and disease conditions through a single system. The models are trained on diverse datasets and can generalize across different scenarios, providing reliable detection without requiring separate specialized systems for each crop or condition.
Solution Approach 2:
The system replaces complex manual monitoring procedures with automated image-based assessment. By using temporal sequences of standard crop images processed through neural networks, the system achieves comprehensive monitoring reliability without the complexity of multiple specialized devices or manual inspection protocols.
Data Source
AI summary
Implementations are described herein for reducing the time and costs associated with the collection and processing of information for observing and evaluating crop growth. In various implementations, a temporal sequence of images depicting a growth of a crop over a time interval may be processed using a machine learning model. Based on the processing, a crop trajectory of image embeddings may be generated that represent the growth of the crop over the time interval. The crop trajectory of image embeddings may be compared with one or more reference crop trajectories of image embeddings. Each of the one or more reference crop trajectories may include a plurality of image embeddings that represent growth of the same type of crop as the crop trajectory of image embeddings over a respective time interval. Data associated with the comparing may be provided as output.