Agricultural Applications
A recent project has demonstrated the effectiveness of AI driven computer vision for agricultural applications.
- Crops (or any kind of organic substance) can be reliably detected, measured and classified/categorized. AI algorithms are surprisingly good at handling problem spaces with fuzzy or ill-defined boundaries. For example, no two potatoes are exactly alike and the quality of a potato is determined by a number of subjective criteria. Nevertheless, smart algorithms could reliably detect, model the 3D shape and classify the quality.
- Pre-harvest analysis is possible for several types of crops. This may allow you to better plan/schedule the harvest and increase your revenue.
- During the entire growth process a large number of variables will determine the final harvest results, many of which can be controlled (humidity, temperature, air flow, fertilizer). Determining the best settings for all these parameters at any point in time is something that’s often done bases solely on experience and “feeling” with the process. AI algorithms can capture this experience and then improve on it by self-learning from results, which in the end inevitably leads to a more optimal system.
- Post-harvest analysis using smart algorithms can improve the sorting of your product; shape, color, texture, bruises, deceases can all be detected as well as a trained worker could do it, in many cases an automatic sorting could be better due to the additional sensor possibilities that are available to a computer (IR, UV, textured light, etc).
Tags: agriculture, computer vision, crops, data mining, fruit, harvest planning, image processing, planning, process control, self learning, vegetables
