Computer Vision in Agriculture

What is Computer Vision?

Computer Vision is a branch of Artificial Intelligence that deals with how machines can gain a high level of understanding from digital images or videos and interpret visual data as a human would. Computers that are equipped with Computer Vision technology can analyze a wide range of images and real-time footage by identifying, describing, predicting, and evaluating particular objects within those visuals. It is, in essence, the human visual system replicated in a machine. Computer Vision is powered by machine learning sub-techniques, notably deep learning and convolutional neural networks.

Computer Vision is a growing area of interest as with other AI techniques. Economic projections estimate that the Computer Vision market will be worth $17.4B in 2023.  

The field of computer vision is developing a firm footing in agriculture.  Computer Vision has enhanced the agriculture field by reducing production costs and improving productivity with its automation and detecting capabilities. 

Why are the Conventional Methods Failing?

Agriculture is regarded as the economic sector that distinguishes any country in the global market. Countries with substantial produce dominate the export market. However, high labor costs, inadequate techniques, and a lack of automation result in higher manufacturing costs.

The use of Computer Vision in agriculture shows the ineffectiveness of traditional inspection methods. The problem with conventional systems is that they rely on human-crewed operations which are:

i. Time-consuming: Traditional agroecosystems need labor-intensive manual input. Observing and estimating crop growth and maturity, for example, are unwieldy if done by hand.

ii. Financially intensive: A bigger workforce comes at a higher expense. As a result, manual operations generate less revenue.

iii. Economically unrewarding: The human factor is always involved in traditional farming practices that are error-prone, affect output, and lower earnings.

iv. Less accurate: Determining the nutritional requirements of every crop might prove problematic when done manually. Therefore, the automation of investment prioritization and decision-making is a necessity for a more accurate allocation of resources.

How can Computer Vision Transform Agriculture?

The key applications of Computer Vision at present are:

i. Object detection

ii. Object tracking

iii. Image classification

iv. Image retrieval 

Each of these key areas is a potential intervention point for Computer Vision in agriculture. A specific area of application is in crop monitoring, an essential agricultural operation.

Manual monitoring of crop growth stages is a labor-intensive procedure that Artificial Intelligence can aid in precision agriculture. There are ongoing attempts to improve crop monitoring using Computer Vision.

In one case, researchers gathered images of wheat at different “heading” phases over three years and in various conditions, to create a “two-step coarse-to-fine wheat ear detection mechanism.” This Computer Vision model exceeded human observation in accurately recognizing wheat development phases. If widely deployed, farmers will no longer need to venture into the fields daily to inspect their crops.

Another research investigated how accurately computer vision can identify tomato ripeness. Researchers developed an algorithm that analyzed color from five distinct parts of the tomato and then predicted ripeness based on this information. The algorithm attained a successful detection and classification rate of 99.31%.

Artificial intelligence has been shown to improve a wide range of operations significantly. Its extensive capabilities are particularly advantageous for revolutionizing legacy sectors such as agriculture. As a result, computer vision could become the keystone of next-generation agronomy.

In coming years, we will be able to handle the dynamic challenges facing agriculture through AI-powered systems which drive us closer to a more sustainable world - a future in which agriculture can benefit from sophisticated autonomous supervision.




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