Machine Vision In Agriculture

machine vision

One of the most rapidly advancing fields in technology is machine vision. Simply put, the phrase “machine vision” means that a computer views an image and analyzes the contents. Coupled with a foundation of artificial intelligence, machine vision extrapolates useful data from the content it views.

In the world of agriculture, machine vision is in use from planting to harvest. As imaging data compiles, information about the crop is used to determine its health and productivity. The machine vision must link with software databases that provide examples for the computer to extrapolate new information. RedBud’s ability to track data provides information to make machine vision more accurate.

The introduction of machine vision into farms across the world is providing farmers with information that is transforming the way their farms operate. Currently, we see real-world applications of machine vision and agriculture intersecting to determine crop yield, harvest quality, diagnostics, and more. As this technology advances, we can expect farmers to utilize smart cameras and AI to guide their farming practices.

One of the most rapidly advancing fields in technology is machine vision. Simply put, the phrase “machine vision” means that a computer views an image and analyzes the contents. Coupled with a foundation of artificial intelligence, machine vision extrapolates useful data from the content it views.

Aerial Images to Predict Crop Yield

Staple crops are crops that determine the food security of nations. In the Western world, wheat and corn are considered staple crops. In the East, rice production is fundamental to keeping the population fed. The ability to track and predict crop yield is of utmost importance when it comes to staple crops.

Researchers at the University of Wuhan in China have begun to utilize drone footage to accurately predict rice yields. Footage obtained by these drones captures multispectral imaging on 6 different bands. The findings of their research show that multispectral images provide a more accurate assessment of crop yield. 

Computers analyzed the images for color spectrum and leaf abundance to give a crop yield prediction that has less than 10% estimation error. This is an incredibly useful tool for determining crop yields. The early detection of potential yield keeps farmers informed. More importantly, it has the potential to stave off situations that could lead to food insecurity for large swaths of the world’s population. 

Harvest Quality Using Smart Cameras

When someone thinks of agriculture, they often envision fields full of crops. But anyone in the industry knows that agricultural work extends beyond farming. One of the most crucial parts of the agriculture industry is determining harvest quality. 

Machine Vision is now being used to analyze the quality of the harvest. Where employees were once needed to manually sort through produce, cameras are now feeding 3D images to machines that can improve the accuracy and efficiency of quality control. 

The University of Technology in Valencia, Spain has created a smart camera that analyzes 3D images of harvested grapes. The function of the camera is to determine the quality of the grapes. An added benefit is that the machine vision identifies unwanted organic matter like stems and leaves which need to be removed before shipping.

Current agricultural quality control demands a large workforce and lacks precision. Smart cameras and machine vision are quickly changing the way crops are processed after harvest. Machine vision-based quality control tools are already becoming widely available. As technology progresses, we can expect them to become an industry standard

camera guided harvest

Machine Vision and Diagnostics

Failure to maintain the health of crops can lead to devastating consequences. It’s become common practice for farmers to spend exorbitant amounts on blanketing their fields in fertilizer, pesticides, and herbicides. This misguided attempt to keep crops healthy is decimating insect life, soil biomes, and the arability of land.

The diagnostic abilities of machine vision seek to remove the need for these expensive inputs. A collaboration between the University of North Dakota and The Upper Midwest Aerospace Consortium used aerial imaging and machine learning to allow farmers to access pertinent information about their crops.

Farmers used the data gathered from the images to make precision applications of fungicides, fertilizers, and herbicides. The diagnostic abilities of the machine vision led the farmers to accurate conclusions about crop health. Overall, the experiment saved the farmers a significant amount of money and protected yields.

Water Absorbtion Imaging

One of the most impactful factors on crop yield is water absorption. Poor watering practices can cause stress that decimates crops. The ability to anticipate and administer the perfect amount of water will soon be a fundamental agricultural process.

The German biotech firm, LemnaTech, has created a protocol using infrared (IR) cameras to track and store data regarding water absorption in crops. The IR camera technology captures images of roots that reveal the uptake of water as well as nutrients. These images are paired with photos of the plant taken above the soil line. The image compilation shows where the water is distributed within the plant.

As data is collected using the information mined from these images, farmers can pinpoint the exact amount of water needed to maintain a healthy crop and conserve resources. The solutions generated by this type of machine vision create a more sustainable industry. It also allows farms to not waste monetary resources on inefficient irrigation.

Future Impacts Of Machine Vision In Agriculture

Soon we can expect that machine vision will further perfect the ability to predict crop yield. Manual inspections to assess plant health won’t be needed, as data collected from imaging will improve remote diagnostics.

Machine vision has the potential to identify the quality of harvests to a degree that will surpass our current inspection methods. As 3D imaging evolves and AI is better trained, poor-quality products will be spotted and discarded without human intervention.

The move toward precision agriculture will demand that irrigation and fertilization practices are carefully considered. Machine vision will play a vital role in factoring exactly how much of these resources are applied.

Don’t let your farm be left behind in the agricultural revolution. Leverage RedBud’s software to harness the power of machine vision, making your farm more efficient, sustainable, and profitable.

From predicting crop yields and diagnosing plant health issues to managing harvest quality and optimizing irrigation, our cutting-edge software can transform every aspect of your farming practices. RedBud Software is designed to integrate seamlessly with AI and machine vision technologies, providing you with precise, actionable insights based on real-time data from your fields.

Take the first step towards a future-proof farming business today. Schedule a free demo with our team, and discover the full potential of RedBud Software. Be part of the innovation. Act now and revolutionize the way you farm!