Modern agriculture is significantly influenced by Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), and Cloud Computing. Particularly in soil health monitoring, crop assessment, nutrient management and surveillance. This paper analyses 164 peer-reviewed research papers published from 2011-2025 with the aim of examining advanced methodologies, performance trends, and limitations in AI-based agricultural systems. The research insights reveal that ML and DL models demonstrated high accuracy in isolated tasks such as prediction of soil parameters, crop disease detection and identification of nutrient deficiency. The existing solutions remain fragmented, cloud-centric, and inaccessible to resource-constrained environments such as agriculture fields of smallholder farmers, especially with respect to wildlife intrusion deterrence. Based on these research gaps, this paper proposes a conceptual Scarecrow AgriBot framework that integrates edge-enabled, intelligent deterrence soil, crop monitoring, nutrient advisory and multilingual farmer interactions within a unified system. This Scarecrow AgriBot is a conceptual framework that serves as a literature-informed foundation for future prototype development and field-level evaluation.