Smart farming uses computer vision (CV) systems to minimize human errors and optimize agricultural processes in agriculture. CV enables machines to interpret visual information, allowing farmers to automate tasks such as weed control, fruit sorting, and harvesting. The technology detects early signs of crop stress, disease, or pest infestations, enabling precise decision-making. Additionally, CV automates tasks like automated sorting, grading, and harvesting, reducing the need for human labor and increasing production efficiency. By analyzing multiple variables, including plant nutrients, pest infestations, fruit ripeness, temperature, rainfall, and soil type, CV algorithms create predictive models that estimate crop yields based on these factors, helping farmers optimize resource allocation and plan better for the harvesting season. The technology also helps with optimal land management by allowing farmers to analyze soil health and suitable plantation areas, enhancing decision-making, efficiency, and sustainability in agriculture.