Computer vision, also known as machine vision, has seen significant advancement in agricultural operations in recent years.
This technology uses algorithms and image processing systems to analyze and extract useful information from images captured in agricultural environments.
Below is an overview of the evolution of computer vision in agriculture:

1. Detection of diseases and pests: Computer vision has proven effective in the early detection of plant diseases and pests. Vision systems can analyze images of crops and detect patterns or anomalies that indicate the presence of diseases or pests. This allows farmers to take preventive measures or apply specific treatments in a timely manner, minimizing damage and optimizing production.
2. Monitoring crop growth and development: Computer vision has made it easier to continuously and accurately monitor crop growth and development. Vision systems can analyze plant images over time to measure parameters such as height, leaf area, density, and coloration. This data provides valuable information for agronomic decision-making, such as the application of fertilizers, irrigation, and pruning, with the goal of maximizing crop productivity and quality.
3. Sorting and grading of fruits and vegetables: Computer vision has been widely used in the automated processing and classification of fruits and vegetables. Vision systems can analyze characteristics such as size, shape, color, and defects of agricultural products. This enables fast and precise product classification according to quality, facilitating marketing and reducing labor costs.
4. Resource management and optimization: Computer vision has also contributed to optimizing resource management on farms. By analyzing images, it is possible to evaluate weed distribution in fields and apply herbicides selectively, thus reducing the use of chemicals and associated costs. In addition, computer vision can help detect areas of water stress in crops, allowing for more precise and efficient irrigation.
5. Agricultural automation and robotics: Computer vision is fundamental in the development of robotic systems for agricultural tasks. Agricultural robots equipped with vision systems can identify and select ripe fruits for harvest, distinguish between crops and weeds, and apply localized treatments based on image-detected needs.

Advances in computer vision and deep learning are expected to drive new image analysis tools, enabling more precise and efficient decision-making. Advances in computer vision and deep learning are expected to drive new image analysis tools, enabling more precise and efficient decision-making. In the future, computer vision will continue to play an increasingly important role in precision and sustainable agriculture.