Precision farming AI solution
About the Client
AgriTech company that develops an end-to-end service for managing, monitoring and optimizing the health and productivity of trees.
The client’s goal was to give farmers a solution for automatically detecting ripe fruits. The solution would optimize inputs, protect the farm’s economic viability and increase the productivity of orchards by preventing tree diseases. It would also gather records about the health and productivity of any individual tree at any time, over time.
To reach this goal, the client started developing a solution that was supposed to increase productivity by more than 40%. This would be achieved with early detection of anomalies and diseases at two levels: an individual tree and the entire plantation.
Having built the basis for the solution, the client collaborated with Quantum to implement more features, fix the ones that were working incorrectly and cover the data science part of the project.
Our client has an AI team that creates a unique service that connects AI cloud technologies and multisensor data operations to reach the best analytical capabilities.
Quantum extended the client’s existing team, increasing the accuracy of analysis and prediction by using sophisticated data science techniques.
The critical components of the system are:
- Image preprocessing.
Alignment of drone images using embedded drone sensors, drone and satellite images stitching and substitution. It gives farmers identical geo-aligned (with accuracy up to 6 cm) images and allows recognizing areas even when time passes.
- Orchards decomposition.
Some models recognize each lane of trees and each tree in it. The resulting dataset can be divided into different sets stratified by the number of oranges per image.
- Growth analysis.
Useful for monitoring each tree, comparing their status with the normal vegetation life cycle, recognizing and calculating fruits or creating other measurements easily.
- Proprietary models.
Our client can use proprietary models for decision-making support. They will save time, reduce costs and help to control the farm business.
Quantum started with the analysis and prototyping phase. Our R&D team did research that gave us an idea of the typical solutions in the field. This helped our client achieve better results.
Quantum provided a model that identifies fruits and the level of tree health from images with a nearly human level of performance.
Our team also highlighted the required metrics for analysis and connected all the data the client received from drones once in a couple of months.
The solution used a neural network to predict crop yields better and, thus, increase the efficiency of farms.
Using the semantic segmentation approach, our client can segment fruit trees on the multispectral drone imagery.
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The main part of this project was written in Python.
OpenCV was used to identify trees and work with images.
After getting pictures from drones, it was important to combine them and analyze, so our team used Python libraries made by Google Keras and Tensorflow for treatment graphs.
The Shapely framework helped to manipulate and analyze planar geometric objects.
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