03 Jul 2025 • 18:34
Agriculture may not be the first place people think of when they hear about machine learning, but it's one of the industries where computer vision is already making a measurable impact.
From improving crop yields to reducing waste, many real-world problems in agriculture are being solved with image-based models, and there’s real income potential—whether you're a founder, engineer, or freelancer.
Below are six real applications of computer vision in agriculture, with examples of how people are earning from them, and practical steps you can take to start building in the space.
Weeds reduce crop yield and quality. Traditionally, farmers have had to spray entire fields with herbicides to keep them under control, which is expensive and environmentally harmful. With computer vision, it’s possible to detect weeds versus crops at the pixel or object level in real time. This allows for precision spraying, where only the affected areas receive treatment.
Companies like Blue River Technology (acquired by John Deere) have built autonomous sprayers that do exactly this. Smaller startups and freelance engineers are now offering similar services on a smaller scale, using drone footage and lightweight models like YOLO or MobileNet.
This is a great space for developers who want to work with real agricultural clients or who want to build a proof-of-concept system using open datasets and drone images.
A growing number of tools now allow farmers to take a picture of a leaf and receive an instant diagnosis of diseases such as blight, mildew, or rust. These systems use image classification and segmentation models trained on thousands of labeled examples of healthy and infected leaves.
Apps like Plaintx have already commercialized this at scale, but there is still demand for localized, crop-specific solutions—especially in developing markets. This creates an opportunity for engineers to fine-tune models on specific crops or regions and deploy them as mobile tools or lightweight web apps.
For those looking to get started, the PlantVillage dataset and similar public resources provide plenty of material for training and experimentation.
In commercial orchards and farms, estimating the number of fruits on trees is crucial for yield forecasting, labor planning, and pricing strategy. Traditionally, this has been done by manual inspection. Now, object detection models can be used to count fruit from drone images or even stationary cameras mounted in greenhouses.
There are open challenges with overlapping fruits, occlusions, and lighting variation, making this a technically interesting problem that still has business relevance. Many farmers and cooperatives are willing to pay for accurate counting solutions that can be deployed on-demand or on-device.
Building a custom YOLOv8-based fruit counter with drone footage is a great technical showcase and can be turned into a portfolio project or freelance offer.
After harvesting, fruits and vegetables are sorted by size, shape, color, or the presence of defects. This process has typically required large, expensive industrial machinery. But smaller operations are increasingly turning to computer vision solutions that use cameras and custom OpenCV pipelines to sort produce on a conveyor belt or table.
These systems can be built using off-the-shelf hardware like USB cameras and microcontrollers, and the software side can be developed with Python and OpenCV. This presents an opportunity to develop low-cost, modular sorting systems that can be adapted for small farms or niche produce types.
Freelancers with both hardware and computer vision skills can offer this as a service or productized tool.
Computer vision is also being used in animal farming, where it's applied to tasks like identifying individual animals, monitoring for injuries, estimating weight, and detecting irregular movement or behavior. Some systems use facial recognition on cows or pigs, while others track patterns of motion using pose estimation.
Edge devices like the Jetson Nano allow for real-time analysis of video streams in barns or open fields. These solutions are especially valuable in farms with limited human oversight or high labor costs.
This is a niche but growing field where technical skills in detection, tracking, and low-latency inference can be applied to tangible problems with measurable return on investment.
While not a complete replacement for laboratory analysis, computer vision models can be trained to detect certain visual cues of soil quality, dryness, or nutrient deficiency based on color, texture, and pattern.
Combined with drone imaging or even smartphone photos, this allows for rapid field assessment across large areas. Some agri-tech startups use this to guide fertilizer or irrigation decisions.
Developers can offer custom tools that analyze images from specific farms or crops, train lightweight classifiers, and build dashboards for agronomists or co-ops.
You don’t need a drone or a farm to begin. Most of these use cases can be explored with publicly available datasets and tools like:
Roboflow: for data annotation and model training
YOLOv8: for real-time object detection
OpenCV: for basic image processing
Firebase + Next.js: for building frontends or dashboards
Google Colab: for rapid prototyping
You can build a simple weed detector or fruit counter in a week, publish your process in a blog post, and use it to demonstrate your skills to potential clients or employers.
There are a few clear ways to monetize this:
Freelance – Offer model training, data labeling, or web-based dashboards to agri-startups or local cooperatives.
Productized Tools – Package your solution (e.g. a fruit counter or weed detector) as a service or downloadable model.
Content + Consultation – Write case studies, tutorials, or courses that attract inbound traffic and convert readers into clients.
You don’t need to wait for perfection. A working demo with good documentation is often enough to land your first client or opportunity.
Agriculture is full of practical problems. And people are already paying for solutions that work.
As a developer, you can pick a narrow problem, build a fast MVP using public tools and datasets, and put it online. The rest is consistency—shipping, writing, improving, and talking to real people.
It’s not easy. But it’s more real than waiting for something to happen.
