Optimizing Smart Agriculture With Cutting-Edge Deep Learning Architectures

Abstract

Agriculture remains a cornerstone of the Indian economy, contributing substantially to national growth and food security. Enhancing agricultural productivity demands improvements in both the quality and quantity of crop yield while minimizing operational costs. One of the major challenges faced by farmers is the prevalence of weeds and pests, which adversely affect crop health and yield. Traditional control methods, including manual weed removal and the extensive use of agrochemicals such as herbicides and pesticides, are labor-intensive, expensive, and potentially harmful to crops and the environment. A more sustainable and cost-effective alternative is selective treatment, which targets weeds, diseases, and pests precisely—requiring an intelligent computer vision system capable of functioning efficiently on resource-limited devices.