Precision agriculture has recently shown a lot of interest in Computer Vision technology. Computer vision, at the heart of robotics and artificial intelligence, enables various operations in the agricultural production cycle, from planting to harvesting, to be carried out automatically and effectively.

However, the lack of publicly available agricultural image datasets continues to be a significant obstacle to the rapid design and assessment of computer vision applications in agriculture and machine learning algorithms for the intended these applications.

Precision agriculture is widely acknowledged as being facilitated by computer vision-based agricultural applications robots and artificial intelligence.

The bulk of duties typically carried out by human-operated agricultural devices or humans might be performed by agricultural robots (such as uncrewed autonomous vehicles, including field surveys, weed control, and harvesting.

Improvements in agricultural computer vision applications

Modern approaches are being adopted by computer vision applications in agriculture to boost productivity and shorten production times, revolutionizing agriculture. We see a few instances of how computer vision has changed modern agriculture below.

Enhancing crop output quality and quantity

High-yield crops that are nutrient-dense and full of vitamins, minerals, and other nutrients that support excellent health are produced using computer vision. Algorithms for computer vision and machine learning in agricullture assist farmers in identifying soil fertility, natural remedies, and insect management. Computer vision technology may also detect tainted food goods and flaws in crop production by classifying objects according to their color, shape, size, and surface texture.

1- Plant illness

Early detection of plant diseases enables farmers to take preventative action. Computer vision technology tracks and categorizes plant leaves across different crops. Genetic algorithms are used in image segmentation, and image recognition approaches to identify plant leaf disease and alert the farmer to problems that need care, thanks to image annotation in agriculture.

2- Fruits and vegetables are graded and sorted.

Computer vision algorithms-trained object identification models with deep learning capabilities categorize and grade objects according to learned criteria. For instance, a deep learning algorithm learns from the data when it is given a batch of “excellent” fruits (let’s use grapes as an example) and another bunch with flaws.

Ultimately, the model will accurately discriminate between poor and excellent grapes: grading and sorting products automatically lower labor costs, delivery times, and physical labor requirements.

3- Phenotyping

Plant phenotyping is carried out using sophisticated computer vision techniques. A camera will be used in this procedure to, at the very least, keep an eye on one to three plants in a row.

The system periodically gathers sample photos to analyze and identify plant characteristics, including height, breadth, color, and anticipated fruit output. Plant phenotyping is then used to calibrate crop models and contribute to a better knowledge of how crops work.

4- Indoor agriculture

Indoor farming is progressing thanks to image processing for precision agriculture. Computer vision and machine learning in agriculture algorithms automate indoor farming in several ways, including soil fertility dynamics, monitoring artificial climatic conditions, evaluating plant growth, and identifying plant illnesses.

The role of farmers as AI engineers in the future of agriculture?

Technology has been employed in agriculture for a very long time to increase productivity and lessen the demanding manual labor needed for farming. Since the advent of farming, humankind and agriculture has evolved, from better plows through irrigation and tractors to contemporary AI.

Computer vision’s expanding and accessible availability might represent a significant advancement in this area.

Given the significant changes in our climate, ecology, and food demands worldwide, AI has the potential to improve 21st-century agriculture by:

1. Improving time, labor, and resource efficiency.

2. Increasing the sustainability of the environment.

3. Improving resource allocation.

4. Providing real-time monitoring to encourage improved produce quality and health.

Of course, the agriculture sector will need to change as a result. Agriculture will need to make significant technological and pedagogical expenditures if farmers effectively integrate the expertise of their “field” into AI training.

However, agricultural ingenuity and adaptability are nothing new. The newest methods for farmers to utilize new technology to fulfill rising global food demands and improve food security include computer vision in in agriculture and agricultural robotics.

Conclusion

In several areas of industrial food production and agricultural farming, computer vision and machine learning in agriculture technologies are already widely used.

The grading of orange, papaya, almond, potato, lemon, wheat, corn, rice, and soybean may all utilize them. Because of the advantages realized, its usage is reasonable. Using such technologies, the samples may be efficiently and impartially analyzed, yielding precise descriptive data.

Through these technologies, tedious processes may be automated and non-destructive, yielding sufficient data for subsequent analysis.

Large amounts of labeled data or image annotaion in agriculture are necessary to train your machine learning models to realize computer vision. It takes a lot of time, effort, and expert-level annotation skills to create datasets that educate autonomous machines, vehicles, and robots on how to succeed both on and off the farm.

Employing a managed and flexible workforce for image annotation in agriculture — a task where Cogito Tech LLC excels — is one-way businesses are improving their computer vision algorithms.

Future Prospects for Computer Vision Applications in Agriculture was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.