Everyone can enjoy the taste of a juicy hand-picked apple from the orchard. What if the whole process, from sowing to harvesting, was completely supervised by a robot without resorting to a single human? Well, computer vision technology is developing at an accelerated rate, making its way into many (nearly all) industries, and agriculture is no exception. We’re going to see the rise of agricultural automation, with the help of computer vision, become a regular occurrence sooner rather than later. Let’s dive in further to understand what new avenues for innovation computer vision in agriculture entails.

Here’s a closer look at what we’ll cover:

  • A quick overview of computer vision
  • Computer vision in agriculture
  • Aerial imaging and scanning
  • Key takeaways

A quick overview of computer vision

Computer vision is a branch of artificial intelligence that allows machines to possess the capability of thinking and interpreting visual data as a human would. Computer vision technology analyzes an array of visuals from images to real-time footage and can track, label, describe, predict and assess particular objects within those visuals. Its aim, especially with the incorporation of deep learning, is to train AI to the point of going beyond simply automating processes. For example, when we look at a scene unfolding in front of us, our eyes make a note of what objects we see, where they are located, where those objects move to (if they move at all), what we can potentially predict happening, and so on. Computer vision technology can incorporate all of that by training artificial intelligence based on existing datasets and pattern recognition.

Specialists expect to see significant development and spread of computer vision applications that will change the modern world as we know it. For example, the notion of shopping may be revolutionized sooner than we think by shifting to cashier-less stores. All of that is possible thanks, for example, to a foundational component of computer vision called semantic segmentation — the technique of assigning a class label to each individual pixel in an image.

computer vision in agriculture

Computer vision in agriculture

Agriculture and agronomy are no exception to the widespread surge of computer vision technology. Adapting machine learning and deep learning to agriculture is a gamechanger since it gives way for more efficient, accurate, and automated processes within the industry, putting us one step closer to the future. Computer vision applications in agriculture, such as detection and classification, combined with state-of-the-art hardware, bring the technology out of the screen and into the field. Technology has always had its place in agriculture, especially on a grand scale. Now, it is becoming more enhanced and polished than ever.

Let’s take a look at specific innovative computer vision applications in agriculture that are more than just a blueprint.

Harvesting with machines

Machines are a time-saving and efficient way to harvest crops, but we aren’t talking about regular farming machines like harvester combines. Specialized robots are equipped with object recognition technology and deep learning to autonomously harvest fruit and vegetables. That is made possible with the combination of two components: First, the act of grasping, which is difficult and is done by specialized hardware, and second, the software or visual part, which identifies the objects to ensure proper grasping.

  • Benefits: This implementation of machine vision in agriculture significantly accelerates process times and reduces the need for manual labor. The latter is especially beneficial during harvest seasons when fruit and vegetables left unattended simply aren’t plucked and decay on the ground.
  • Challenges: Fruit detection is one of the most difficult tasks in agricultural automation. The complications are a result of unpredictable variables involved in the process that can skew results, such as inconsistent illumination, poor visibility or occlusion due to foliage, and inconsistencies in the shape of the fruit or vegetable.

Grading and sorting

Assessing features such as ripeness, color, size, and defects is a task that pomologists and other workers in agriculture carry out quite often for quality assurance of their harvest. Furthermore, they assess the needs of a particular crop, such as whether it needs more water or different antibiotics doses. What if we told you there is ready-made technology that deals with the scanning and labeling of fruit and vegetables in real-time?

annotated strawberry
  • Benefits: Traditional grading and sorting is human-dependent, labor-intensive, and takes up significant time to execute for large quantities of harvested produce. More manpower is required for larger farms and manufacturers that aim to sort and grade hundreds of thousands of produce each day. That is where computer vision comes in to innovate the process via scanners equipped with image classification technology.
  • Challenges: Initial technology and updated prototypes yield promising results but need to be optimized for a number of specific shortcomings. Firstly, scanning of the produce via a 2D image may result in inconsistencies if the produce, for example, a tomato, has an apparent marking on the other side that was left unscanned. Additionally, the biological variation among the fruits and vegetables must be considered to yield accurate results.

Aerial imaging and scanning

Unmanned aerial vehicles (UAVs) are becoming widely utilized in recent years for tasks such as remote sensing and fire detection. There’s no stopping there since aerial imaging and scanning with the help of computer vision technology aims to establish what will be known as “farming vision”. The use of UAVs especially aims to be an asset for precision agriculture, otherwise referred to as aerial agriculture. Precision agriculture aims to boost eco-friendliness and efficiency while reducing the amount of traditional input required to cultivate crops such as land, water, fertilizer, herbicides, and insecticides. That can be possible through precise aerial imaging and mapping to assess each region based on the results of the imaging.

  • Benefits: UAVs equipped with extensively trained computer vision models are the key to smart farming by carrying out processes like vegetation classification and segmentation, crop counting, yield monitoring and prediction, crop mapping, weed identification, and disease and nutrient shortage detection. Essentially, they allow farmers and field workers to engage with crops remotely and only interfere when the data acquired from the UAVs deem necessary.
  • Challenges: There are still limitations to be addressed with computer vision in aerial agriculture. That includes the nuances of plant dispositions across a set period of time. For example, different plants go through different phases throughout the seasons, at different times, for a variety of intervals. The technology must be foolproof in its deep learning capabilities to assess these spatial changes without compromising on accuracy.
computer vision in harvesting

Plant phenotyping

Plant phenotyping is a task that falls under precision agriculture that involves the systematic evaluation of plant characteristics such as growth, development, adaptability, quality, tolerance, resistance, and structure. It is similar to the computer vision technology used for grading and sorting however it expands to help the research of botanists as well.

  • Benefits: Once again, this form of agricultural automation minimizes the manual labor required by specialists on the field to analyze each plant one by one. With the help of such technology, those working in the field can quickly acquire the data, assess the annotated information, and then take samples of plants they believe have diseases to the lab for testing.
  • Challenges: While computer vision models showcase great prospects for accuracy, detection, and automation, this mass of data still requires significant input by scientists. The AI must be trained to account for weather changes, plant species variations, lighting changes, and an entire plethora of diverse scientific data required to accurately assess the plants. It is still a task that requires the supervision and investment of specialists before the technology can be fully self-sufficient.
annotated oranges

Key takeaways

Overall, the impact of computer vision in agriculture is on a positive incline since there is more progress being made than setbacks. The preexisting and currently developing applications of computer vision in agriculture cut down on manual labor in the field, increasing the speed and accuracy of operations. Agriculture no longer needs to be associated with long hours in the field conducting grading and scanning of fruits and vegetables by hand or straining physical health. Crops may be tended for remotely, harvests collected effectively, and mass production of goods will be accelerated, all thanks to AI. Farmers, pomologists, botanists, and many more can dedicate their time to other processes in their field of work. Their presence in the field won’t become redundant with the rise of AI in agriculture since they are essential for the process of training datasets and assessing anomalies in data acquired via machine learning or deep learning technology. Tools are at the foundation of agriculture, and with computer vision, those tools can only become more enhanced and take agriculture to the next level on an international scale.

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