Computer Vision in Agriculture

Mechanized agriculture has delivered astonishing productivity advances, yet many sorting and selection tasks and a host of related activities continue to rely on human intervention. LandingLens enables new computer vision solutions in agriculture to be introduced easily and rapidly, helping to increase throughput, cut costs, and improve quality.

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Applications of Computer
Vision in Agriculture

LandingLens enables always-on, continuous inspection and analysis for images streams. With very low false positives, LandingLens introduces highly reliable AI-powered computer vision and machine learning solutions for every aspect of agriculture and food production, such as grading, sorting, labeling, counting, and much more.

Grading and Sorting

LandingLens enables high-volume grading and sorting, with high accuracy. The LandingLens platform can be trained, configured, and deployed quickly and easily, and fully integrated with existing agricultural production systems. Using existing datasets for a huge range of fruits, vegetables, and cereals, as well as meat and meat products, LandingLens automates repetitive grading and sorting, offering high throughput, with reliable and consistent results.

Labeling and Counting

From animal monitoring on livestock farms to product volume counting, LandingLens automates essential tasks throughout the value chain. For example, LandingLens can integrate into an imaging system to count animals, assess activity, monitor health, and more. Even as agriculture teams are asked to manage larger livestock numbers, LandingLens can deliver greater productivity with efficient labeling and counting. Our sophisticated computer vision platform enables rapid deployment and excellent accuracy for all agricultural scenarios.

Yield Estimation

Yield estimation forms an essential part of agricultural production planning, for labor, inputs, transportation, and more, with direct effects on profitability. However, manual product counts – such as for fruits, flowers, and vegetables – can be time-intensive, relatively unreliable (some people are more diligent than others!) and labor-intensive. Computer vision driven by LandingLens enables highly accurate crop counts in farming from captured images. LandingLens provides reliable, repeatable estimates to help improve agricultural planning, increase productivity, and improve profitability.

Disease Inspection

Identifying, understanding, and treating disease at the earliest possible stage can prove to be a game-changer. Computer vision can support very rapid data-gathering, with real-time analysis and reports on disease incidence, types, and progression. For both livestock and crops, LandingLens incorporates very large reference databases that enable high-accuracy diagnosis from even relatively small image samples. By removing manual expense and effort, disease management enabled by LandingLens contributes directly to improved yields, reduced costs, and greater productivity.

LandingLens Benefits for Computer Vision
and AI in Agriculture

LandingLens democratizes computer-aided vision by making it super-easy to configure, train, and deploy your own tailored agricultural application. The end-to-end LandingLens platform standardizes deep learning solutions, helping to cut development time, enable near-limitless scaling, and deliver rapid results. Learn more about the benefits of computer vision in agriculture:

Reduce Manual Workload

Many agricultural tasks require simple counting, perhaps of livestock, ripe fruit, or sown fields. LandingLens provides high-accuracy automation for counting tasks that greatly reduces the manual counting workload. In addition, the underlying AI can complete multiple tasks simultaneously, such as checking for disease or monitoring behavior.

LandingLens provides actionable data that can be used to plan the best allocation of resources. For example, data provided by LandingLens helps identify when disease treatments should be applied, or (for livestock) when food levels are low, helping to ensure efficient operations, optimize production, and enhance profitability.

Improve Total Productivity

Maximizing agricultural efficiency depends on a vastly complex array of interactions, from irrigation to soil defect diagnosis, from animal health to input feed costs. For example, successfully targeted irrigation can lead to higher yields, reduced water consumption, and improved soil condition. Similarly, for livestock early disease detection can prevent veterinary costs, reduce losses, and improve sale prices.

By deploying AI-powered computer vision from LandingLens to monitor crop and livestock health, pests, yields, and much more, many manual tasks can be replaced with accurate, reliable automation at much lower cost, helping to improve total agricultural productivity and profitability.

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Case Studies

Computer Vision in Agriculture

Applications of Computer
Vision in Agriculture

Grading and Sorting

Labeling and Counting

Yield Estimation

Disease Inspection

Reduce Manual Workload

Improve Total Productivity

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Computer Vision in Agriculture

Applications of ComputerVision in Agriculture

Grading and Sorting

Labeling and Counting

Yield Estimation

Disease Inspection

Reduce Manual Workload

Improve Total Productivity

LandingLens: Artificial Intelligence and Food and Beverage Packaging Inspection

Deep Learning Playbook for Manufacturing Industries

Deep learning AI Platform – Build vs. Buy?

Applications of Computer
Vision in Agriculture