Agricultural economics; Artificial Intelligence; Systematic Review; Con-volutional Neural Networks; System Development; Multi-crop.
Brazilian fruit farming stands out in agribusiness due to its productive diversity, wide territorial distribution, and high socioeconomic relevance. Despite this leading role, the sector presents structural vulnerabilities related to market volatility, productive concentration, and high sensitivity to climatic and phytosanitary factors, compromising production stability and revenue predictability, especially in perishable product chains. In this context, the integrated analysis of economic performance and technical factors becomes essential to guide strategic decisions and promote greater productive efficiency. Economic evaluation allows the identifi-cation of growth patterns, concentration levels, and crop performance, supporting the prioriti-zation of more relevant segments. However, the sector's sustainability depends not only on pro-ductive expansion but also on mitigating losses, especially those of phytosanitary origin. Tra-ditional diagnosis, based on visual inspection, presents limitations such as subjectivity, depend-ence on specialists, and low scalability. Therefore, Artificial Intelligence techniques, especially Computer Vision and Deep Learning, emerge as innovative solutions for the automated monitoring of diseases. Integrating these technologies into production systems enables greater diagnostic accuracy, early detection, and support for data-driven decision-making, contributing to reduced losses and increased competitiveness in fruit farming.