Título:	Hail netting in apple orchards: current knowledge, research gaps, and perspectives for digital agriculture.
Autor:	FURUYA, D. E. G. BOLFE, E. L. SILVEIRA, F. da BARBEDO, J. G. A. SILVA, T. L. da ROMANI, L. A. S. CASTANHEIRO, L. F. GEBLER, L.
Afiliación:	DANIELLE ELIS GARCIA FURUYA; EDSON LUIS BOLFE, CNPTIA; FRANCO DA SILVEIRA; JAYME GARCIA ARNAL BARBEDO, CNPTIA; TAMIRES LIMA DA SILVA, UNIVERSIDADE ESTADUAL PAULISTA "JÚLIO DE MESQUITA FILHO"; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; LETÍCIA FERRARI CASTANHEIRO; LUCIANO GEBLER, CNPUV.
Año:	2025
Referencia:	Climate, v. 13, n. 10, 203, Oct. 2025.
Descripción:	Hailstorms are a major climatic threat to apple production, causing substantial economic losses in orchards worldwide. Anti-hail nets have been increasingly adopted to mitigate this risk, but the scientific literature on their effectiveness and future applications remains scattered, especially considering advances in digital agriculture. This study synthesizes current knowledge on the use of anti-hail nets in apple orchards through a systematic review and explores future perspectives involving digital technologies. A PRISMA-based review was conducted using three databases, revealing information regarding the studied countries, netting colors, and apple varieties, among others. A clear research gap was identified in integrating anti-hail nets with remote sensing and Artificial Intelligence (AI). This paper also analyzes studies from Vacaria, Brazil, a key apple-producing region and part of the Semear Digital project, highlighting local efforts to use hail netting in commercial orchards. Potential applications of AI algorithms and remote sensing are proposed for hail netting assessment, orchard monitoring, and decision-making support. These technologies can improve predictive modeling, quantify areas, and enhance precision management. Findings suggest combining traditional protective methods with technological innovations to strengthen orchard resilience in regions exposed to extreme weather.
Thesagro:	Malus Domestica Sensoriamento Remoto Maçã
NAL Thesaurus:	Climate Remote sensing
Palabras clave:	Pomar de maçã PRISMA Clima extremo Aprendizado de máquina Aprendizado profundo Agricultura digital Extreme weather Deep learning Machine learning Digital agriculture
DOI:	https://doi.org/10.3390/cli13100203
Tipo de Material:	Artigo de periódico
Acceso:	openAccess
Aparece en las colecciones:	Artigo em periódico indexado (CNPTIA)