American International Journal of Research and Innovation

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Artificial Intelligence for Crop Disease Detection and Yield Prediction

Author(s) Dr. Maria Santos Reyes
Country United States
Abstract Agricultural productivity is increasingly affected by climate variability, emerging crop diseases, changing environmental conditions, and the growing demand for food production. Traditional crop monitoring methods often depend on manual observation, which may result in delayed disease identification, inefficient management decisions, and reduced yield potential. Artificial Intelligence (AI) has emerged as a transformative technology for modern agriculture by enabling automated crop disease detection, predictive analytics, and intelligent yield forecasting.
This study examines the application of Artificial Intelligence for crop disease detection and yield prediction through a conceptual analysis of machine learning algorithms, deep learning models, computer vision systems, remote sensing technologies, and agricultural data analytics. The research explores how AI-based approaches improve early disease identification, optimize farm management decisions, and enhance crop productivity.
The study adopts an analytical framework based on artificial intelligence, precision agriculture, digital farming, and sustainable agricultural management principles. The findings indicate that AI technologies improve agricultural decision-making through accurate image-based disease recognition, environmental data analysis, and predictive yield modelling. However, challenges related to data availability, computational requirements, technology costs, model accuracy, and farmer adoption affect large-scale implementation.
The study concludes that Artificial Intelligence provides significant opportunities for developing intelligent agricultural systems capable of improving crop health monitoring and yield prediction. Integrating AI technologies with farmer knowledge, digital infrastructure, and sustainable farming practices can support efficient, resilient, and productive agricultural systems.
Keywords Artificial Intelligence, Crop Disease Detection, Yield Prediction, Machine Learning, Deep Learning, Smart Agriculture, Precision Farming.
Field Engineering
Published In Volume 4, Issue 5, September-October 2022
Published On 2022-10-23
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