Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups working on solutions for the agriculture industry. As there is a large number of startups working on a wide variety of solutions, we decided to share our insights with you. This time, we are taking a look at 5 promising Computer Vision Startups.
Heat Map: 5 Top Computer Vision Startups
For our 5 top picks, we used a data-driven startup scouting approach to identify the most relevant solutions globally. The Global Startup Heat Map below highlights 5 interesting examples out of 21 relevant solutions. Depending on your specific needs, your top picks might look entirely different.
XSUN – Aerial Survey & Imaging
Aerial field imaging is continuously developing as more artificial intelligence (AI) applications get bundled onto a diverse range of drones built specifically for agriculture. Computer vision is one such technology. It combines high-resolution image capturing with high-speed computing to break down images and video that helps the farmer make informed decisions about the farm. French startup XSun develops SolarXOne, a solar-powered autonomous flying vehicle that can cover up to 200 acres at a single fly. Their HD images provide the farmer with a precise view of critical farmlands. The multispectral images capture detailed crop and soil conditions to monitor for stress and disease. They also use hyperspectral images, which can capture images at 20 times higher wavelengths compared to multispectral images, to capture the chemical composition of soils.
TerraClear – Clearing Rocks
Farmers have to deal with many land-related issues before actually sowing crops. A particularly painful and time-consuming task is to clear the sowing field of rocks. Delegating such basic tasks to robotics and automation will save valuable time for the farmer to focus on more productive tasks around the farm. The US-based startup TerraClear combines aerial sensing, machine vision, high-accuracy GPS, and advanced robotics to develop an end-to-end rock picking solution.
SWIR Vision Systems – Monitoring Soil Moisture
Soil and water management are at the core of agriculture’s success. Unprecedented stress on these natural resources has forced humans to think about ways to solve for, move away from, and even artificially mimic the conditions that plants need to grow. Computer vision, combined with AI, is being rapidly developed for farmers to use their resources more judiciously. The US-based startup SWIR Vision Systems develops Acuros, a family of high-definition cameras that deliver visible-to-SWIR band – wavelengths between 1.400 and 3.000 nanometers. These cameras also hold sensors to provide vital agricultural services like water stress and moisture measurement and irrigation control.
Cromai – Farm & Crop Diagnostics
There is a plethora of data being collected by technology companies from real farms. This data can be in the form of information on a smartphone, data from sensors and equipment, and data captured by drones and satellites. There is a group of innovative startups who develop AI-based hybrid solutions for farm data collection and management. Brazilian startup Cromai develops AI-based solutions to provide the farmer with diagnostic information about the land and crops. They use computer vision to capture the color, shape, and texture of crops to further analyze them. They also provide diagnostic information collected from sensors placed on tractors or other equipment.
Occipital Technologies – Grading & Sorting
When the time comes to harvest the farm, a lot of produce gets wasted or spoiled before it can reach the distributors. Considering the high manpower requirements to complete harvest tasks quickly, post-agricultural cycles end up leaving the farmer feeling quite stressed. Advancements in automation technologies along with AI-based solutions can save a lot of time and money for the farmers. Indian startup Occipital Technologies develop technologies with the goal of reducing food loss and labor costs involved in post-harvest cycles in agricultural commodities. They provide automated grading and sorting solutions using computer vision and machine learning. They aim to automate quality control in agriculture by grading and sorting agricultural commodities based on their physical parameters and quality.
What About The Other 16 Solutions?
While we believe data is key to creating insights it can be easy to be overwhelmed by it. Our ambition is to create a comprehensive overview and provide actionable innovation intelligence for your Proof of Concept (PoC), partnership, or investment targets. The 5 startups showcased above are promising examples out of 21 we analyzed for this article. To identify the most relevant solutions based on your specific criteria and collaboration strategy, get in touch.