Artificial intelligence-assisted technologies in horticultural crop management

Abstract

Horticultural crops are essential for human well-being, as they provide crucial nutrients that support a balanced diet, enrich cultural landscapes, and contribute to nutritional security. To meet the demands of growing population, it is imperative to enhance both the quantity and quality of horticultural produce while considering limited stakeholder values. Integrating artificial intelligence (AI) into horticultural crop management is essential for optimizing productivity, diagnosing diseases, and enhancing value-addition. AI can help understand domestication events, adaptation processes, yield improvement, pathogen detection, and resilience by rapidly analyzing vast amounts of data from high-throughput sensors, satellite imagery, and climate models. By assimilating diverse data sets, deep learning technologies offer reliable predictive outcomes for complex and uncertain phenomena. The applicability of advanced tools and technologies for significant improvements in horticultural crops and their monitoring through sustainable practices is discussed. These technologies facilitate effective classification and recognition of images of flowers, fruits, and vegetables, as well as phenotypic
identification based on various attributes, enhancing productivity and economic returns. Furthermore, they leverage the horticulture industry through automated precision farming, predictive analytics, and supply chain optimization; ultimately promoting resource-efficient and sustainable practices to advance the Horticulture Sector.