CROP DISEASE DETECTION AND YIELD PREDICTION USING COMPUTER VISION

Crop diseases still pose one of the greatest threats to food security, particularly in regions where agriculture is the
primary economic driver. Conventional disease surveillance systems are manual-based and thus time-consuming,
labor-intensive, and subject to human error. The latest progress in computer vision and machine learning has created
a new opportunity to detect the diseases of crops early and forecast yield, so farmers may respond appropriately in
time and produce more. In this paper, the author discusses the application of computer vision in agriculture in two
important tasks, i.e., disease detection and yield prediction. Deep learning models are able to perceive visual patterns
of infection not easily seen by humans by analyzing leaves, stems, and fruit with image analysis. Equally, forecasting
models of yield prediction through visual images and environment predict more precise production compared to
traditional methods. The paper outlines the principles and methods of computer vision, as well as its potential
applications in agriculture, and compares its benefits to those of traditional technologies. In addition, it also discusses
the questions that relate to the quality of data, the interpretability of the model, and its application in real farm
scenarios. The results highlight that computer vision is not only revolutionizing the process of disease diagnosis but
also shaping the future of precision farming and food production.