Computer Vision in Agriculture
AI Solution for Detecting Diseases in Banana Leaves
The client engaged Timspark to develop an innovative AI solution to monitor and control banana cultivation in greenhouses and plantations, automating agronomist tasks and promptly detecting issues. Their specific idea was a computer vision system to automatically inspect and characterize banana seedling leaf damage, but the main challenge was a lack of suitable training data.
#ML
#Web
#ComputerVision
Client*
Top banana seeding supplier in the world
*We cannot provide any information about the client or specifics of the case study due to non-disclosure agreement (NDA) restrictions.
Project in numbers
duration
4 months
team
5 specialists
The team involved in the project
industry
Agriculture
Web, ML
Python, Pandas, NumPy, Pytorch, Streamlit, OpenCV
1 x Lead Data Scientist
1 x Data Scientist
1 x Data Engineer
1 x Project Manager
1 x QA
Challenge
The client was concerned about the possibility of using computer vision in agriculture to detect plant disease and prevent crop loss automatically. On the tech side of things, our team faced a scarcity of data available to train AI-based computer vision solutions.
Solution & functionality
Following our advice, the client strategically installed object-detecting cameras in the greenhouse, mainly above and to the sides, to periodically capture images of banana seedlings. The client has the flexibility to modify the frequency of these image captures as desired.
Deep learning for object detection and classification
Our team used Python and PyTorch to develop and implement a software module that extracts images of detected leaves and sends them to the classifier for further analysis. Timspark’s specialists were responsible for developing and optimizing the classifier model to meet the project’s unique requirements.
Plant condition reports and analytics
Once images are received, the classifier model proceeds with a systematic classification, carefully separating damaged leaves from healthy ones. Following this analysis, the model generates a comprehensive PDF report that provides a complete picture of the plant’s condition. Moreover, it provides practical care recommendations to help agronomists optimize banana seedling development.
Results and business value
Not only did Timspark create and enhance the classifying model, but they did so with great care, tailoring it exactly to the project’s particular requirements. The client is very pleased with the result and maintains an ongoing partnership with us for future work.
Benefits for client
Thanks to computer vision services, the customer has successfully put this technology to work and significantly cut down on banana seedling losses.
Related cases
FAQ
Here are the case study relevant questions and answers.
Why do you use Python for ML and computer vision projects?
Python has become the de facto standard for machine learning and computer vision due to its extensive ecosystem of libraries and tools. In this agricultural project, for example, the solution was built with Python, Pandas, NumPy, PyTorch, Streamlit, and OpenCV — covering everything from data processing and deep learning to image handling and the web UI.
Libraries like PyTorch and OpenCV are specifically optimized for AI and vision workloads, while NumPy/Pandas handle fast numerical and tabular operations. Together, they let teams prototype quickly, iterate on models, and then move the same codebase toward production without rewriting everything in another language. The strong community and open-source tooling also keep licensing costs down compared to fully proprietary stacks.
What are classification models?
It's a type of supervised machine learning algorithm that assigns data points to predefined categories or "classes".
Who can benefit from ML classification models and computer vision solutions like this?
Any organization that has a lot of images or videos (or structured events that can be “classified”) can benefit from ML models similar to those used for leaf damage detection in agriculture. Typical beneficiaries include: Agriculture, Manufacturing & industry, Healthcare, Retail & logistics, Security & transportation.
How much does it typically cost to build a custom computer vision / ML solution similar to this?
Costs vary a lot, but there are some realistic brackets. Market guides for custom computer vision projects usually put:
- Smaller / simpler projects (narrow scope, clear data, limited integrations) in the ~$15,000–$50,000+ range.
- Real-time video / more advanced solutions in the $40,000–$100,000+ range.
- Complex, multi-module AI systems can easily exceed six figures, especially with strict compliance requirements or large deployments.
In the banana-leaf project, the delivery team consisted of 5 specialists over 4 months (Lead Data Scientist, Data Scientist, Data Engineer, PM, QA), which is a typical setup for a custom ML + web solution.
A similar scope would usually put a client into the "small to mid-size" custom AI project bracket.
- The final number depends mainly on:
- How much data collection and labeling is needed
- The complexity of the model and accuracy targets
- Required integrations (existing software, hardware, sensors, cameras)
- Whether you need just a PoC/MVP or a fully hardened production system with monitoring and support
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