Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups working on solutions for the agriculture sector. As there is a large number of startups working on a wide variety of solutions, we want to share our insights with you. This time, we are taking a look at 5 promising deep learning startups.
Heat Map: 5 Top Deep Learning 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 272 relevant solutions. Depending on your specific needs, your top picks might look entirely different.
Beriqo – Agri Information Management System
Agricultural productivity depends on a lot of environmental variables such as weather, groundwater reserves, and soil health. These cause differences in yield even in nearby fields or, sometimes, in different areas of the same field. Precision agriculture aims to minimize this variability by combining remote sensing with artificial intelligence (AI).
The Canadian startup Beriqo develops an information management system (IMS) for agriculture. The startup uses powerful deep learning algorithms to detect pixel-by-pixel changes in stacks of satellite images collected weekly. Their analysis enables decision making in precision agriculture by identifying parameters such as underperforming fields, nutrient deficiency, pest damage, and missed fertilizer stripes.
AbuErdan – Livestock Monitoring
Animal welfare in the poultry industry focuses on the care of individual chickens at a farm. It mitigates some ethical concerns, improves community health, and increases yield. However, round-the-clock manual monitoring of all chicken is an uphill task. Deep learning algorithms that analyze camera feeds to monitor all birds are attractive solutions.
AbuErdan is a US-based startup that builds a livestock management system for poultry. The system uses deep learning networks and predictive analytics to predict the performance of chicken from past performance and historical data while also enabling better breeding decisions to improve the quality of poultry. The startup’s solution additionally helps breeders plan and manage the entire poultry value chain.
Klimazone – Climate Recipes
Variability between different plants of the same crop causes differences in their growth and yield. Growing them in controlled environments minimizes this variability. However, it is not a feasible solution for large tracts of land. Vertical farming and climate recipes address the limitations of space and variability, respectively. A climate recipe is a set of environmental conditions that cause a desired outcome in the plants.
German startup Klimazone creates big data-based solutions for vertical farming. Their deep learning-based monitoring system allows growers to create inexpensive climate recipes for controlled growth environments. The solutions sync food production with sourcing to minimize wastage and precisely monitor plant health and growth for better yield. Klimazone’s solution is easily scalable and makes vertical farming affordable.
Bilberry – Intelligent Spot Spraying System
Farmers use herbicides to eliminate weed growth in agriculture. However, their unchecked use has a considerable environmental impact and contributes to the development of herbicide-resistance among weeds. Solutions that replace carpet spraying of herbicides with more precise use reduce costs and mitigate undesired effects that come from their overuse.
Bilberry is a French startup that develops an intelligent spraying system for efficient weed control. The startup uses cameras mounted on sprayers and deep learning-based recognition algorithms for precise, real-time application of herbicides. It also maps weed distribution to improve its precision with use and for better agricultural process management.
AgroScout – Image-Based Anomaly Detection
Plant diseases such as blight and gall ruin a lot of crops annually. Symptoms of these diseases are often first noticed in leaves as spots or lesions. However, the wide variety of leaves makes general detection algorithms impractical. Deep learning algorithms overcome this challenge and detect crop diseases by scanning leaves.
Israeli startup AgroScout works on an image-based anomaly detection solution to detect crop diseases from visual inspection of leaves. Their platform is cloud-based and collects images from both drones and smartphones. It also incorporates data from weather, satellite, and other local sensors. The algorithms detect diseases in early-stage and, thus, reduce pesticide use costs.
What About The Other 267 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 deep learning startups showcased above are promising examples out of 272 we analyzed for this article. To identify the most relevant solutions based on your specific criteria and collaboration strategy, get in touch.