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Palabras clave – provistas por la editorial

grape varieties identification; Support Vector Machine (SVM); Convolutional Neural Network (CNN); deep feature fusion; Canonical Correlation Analysis (CCA); smart machinery; digital agriculture; Chinese agricultural diseases and pests; named entity recognition; adversarial training; semantic enhancement; technology innovation; food processing; transition pathways; sustainable food systems; transformation; smart farming; IoT; WSN; containerization; multi-agent; neural network; LSTM; leisure agricultural park; traveler group; COVID-19 pandemic; fuzzy collaborative intelligence; machine vision; maize seeds; classification; deep learning; convolutional neural network; decision support systems; agricultural water management; water security; data-driven modeling; conceptual resilience model; input uncertainty; climate extreme; process-based modeling; vehicle routing problem; fresh agricultural products; split delivery; NSGA-II algorithm; farm management information system; farmers’ information needs assessment; soft system methodology; smallholder farmers; conceptual model; Indonesian chili farmers; residual block; attention mechanism; grape leaf disease; aquatic products price forecast; VMD; IBES; hybrid model; precision agriculture; sensor network; semi-literate farmers; interactive interface; User Interface (UI); Android apps; machine learning; regression algorithms; web application; early prediction of crop yield; grape detection; self-attention; buffalo breeds; Neural Networks; Self Activated CNN; DeepLabv3+; semantic segmentation; picking point identification; e-commerce interest linkage; participation willingness and behaviors; government policies; farmers’ cognition; evolutionary game model; structural equation model; object detection; YOLOv7; hemp duck count; smart agriculture; LoRaWAN; water status; supply chain; horticulture; logistics; operations; planning framework; decision support; n/a