Abstract
Integrating Artificial Intelligence (AI) into agricultural technology has revolutionized crop management, offering sophisticated tools for optimizing crop yield predictions. This paper explores the methodologies, benefits, and challenges associated with using AI-driven models to enhance the accuracy of crop yield forecasts. Through a comprehensive review of existing literature and case studies, this research highlights the potential of AI to improve food security and sustainability in agriculture. AI technologies in agriculture show promise for improving crop management techniques and production outcomes. This article examines how AI systems might maximize crop production by monitoring, detecting diseases, managing irrigation, and predicting yields. This study examines the impact of AI on agricultural practices by reviewing relevant literature, case studies, and technological advancements. AI algorithms, machine learning, and remote sensing technologies enable farmers to make data-driven decisions, optimize resource use, and mitigate environmental hazards, resulting in sustainable farming practices and improved food security.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 1
Claim · model-written summary
The review concludes that AI technologies show promise for improving crop management and production outcomes.
Verbatim
e0“AI technologies in agriculture show promise for improving crop management techniques and production outcomes.”
t0“Crop Harvest Prediction”