Advanced Convolutional Neural Networks for Detecting and Quantifying Infections in Coconut Tree Tissue Cultures

Coconut tree cultivation is crucial for many tropical cultures, serving as a vital source of nutrition, income, and environmental stability. However, the traditional methods used in coconut tree tissue culture are not without challenges. Issues such as bacterial and fungal infections can significantly hamper the growth and productivity of coconut trees, limiting the effectiveness of tissue culture practices. These infections often go undetected in their early stages, making it difficult for farmers to take timely and effective action. This research proposes a novel approach to improving coconut tree tissue culture by integrating machine learning with agronomical expertise. Through advanced image analysis, the study enhances the identification and severity prediction of bacterial and fungal infections in tissue cultures. These insights lead to actionable recommendations for healthier trees and more efficient cultivation. Integrating technology with traditional expertise marks a significant advancement in addressing challenges faced by coconut farmers, offering a pathway to increased productivity, sustainability, and improved infection management, benefiting both farmers and the broader agricultural community.