The agricultural sector performs an indispensable role in universal sustenance and financial advancement. Still, diverse diseases, comprising those triggered by disease-causing agents such as bacteria, viruses, and fungi, usually threaten the robustness and proficiency of crops. Precocious detection and accurate prognosis of these infections are necessary for efficient disease supervision and governance strategies. In this framework, advanced methods and deep learning strategies, such as the VGG19 structure, provide optimistic resolutions for AI-driven disease identification in vegetation. VGG19, a continuation of the Visual Geometry Group (VGG) clan, utilizes transfer learning to attain high accuracy in feature exclusion and image classification projects. By capitalizing on its proficiencies, remarkably in spotting evidence like mottled leaves, leaf disfigurement, retarded growth, leaf curving, and leaf tarnishing, VGG19 illustrates a consequential perspective in determining diseases such as mosaic and curl pathogens. These infections, transferred by means of bug vectors and infected plant substances, impose substantial threats to crop wellness. This research paper targets to investigate the program of VGG19 in discerning mosaic and curl pathogens in vegetation, with a target on augmenting accuracy and effectiveness contrasted to conventional recognition approaches. By harnessing the control of deep learning and image taxonomy, this study pursues the advancement of robust disease-supervising approaches, guaranteeing the well-being and efficiency of agricultural and planting vegetation.