LEVERAGING ARTIFICIAL INTELLIGENCE FOR PRECISION AGRICULTURE: APPLICATIONS IN BOTANY

Background: The implementation of artificial intelligence (AI) systems in precision agriculture attracts substantial research because they have shown promise in both improving farming operations along yield growth. The research explores AI applications within botany with a specific focus on crop health monitoring and soil analysis alongside weather prediction algorithms accurate decision systems and yield optimization processes. Objectives: This research sets out to establish quantitative correlations between AI-based crop health tracking mechanisms AI-based soil testing approaches AI-provided weather prediction solutions and decision precision and improved crop yield outcomes. This research examines how well AI solutions handle essential agricultural problems. Methods: Researchers used a structured survey instrument to collect data from three groups including farmers and researchers alongside technology professionals among 355 participants. The questionnaire contained rating scale items distributed on a 5-point Likert scale. Team researchers used descriptive statistics alongside the Shapiro-Wilk test and Cronbach's Alpha to analyze the data. Results: All variables exhibited significant deviations from normality according to the Shapiro-Wilk test (p < 0.05) requiring the use of non-parametric analysis techniques. Results showed a Cronbach's Alpha rating of 0.57 which reflects moderate unitary scale consistency within the survey instrument. The research demonstrates how stakeholders experience different levels of interaction with AI systems while revealing gaps in current measurement instruments. Conclusions: The analysis presents clear evidence about how AI technology can boost farming outcomes and yield optimization initiatives in precision agriculture operations. The current research needs better design approaches because it revealed moderate questionnaire reliability and non-normal distribution of data points. Researchers agree that AI systems must deliver customized solutions that address the requirements and obstacles faced by different members of the agricultural community. Future research priorities include developing enhanced data collection procedures together with non-parametric data analysis solutions and solutions to barriers impeding agricultural AI adoption.