Título:	AgDataBox-Map: web application for spatial analysis and management zone delineation for dynamic fruit harvest in precision agriculture.
Autor:	FARIA, C. B. de MARCHIORETTO, L. de R. GEBLER, L. BAZZI, C. L.
Afiliación:	CAROLINE BOLSON DE FARIA, EMBRAPA UVA E VINHO; LUCAS DE ROSS MARCHIORETTO, EMBRAPA UVA E VINHO; LUCIANO GEBLER, CNPUV; CLAUDIO LEONES BAZZI, UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ.
Año:	2026
Referencia:	In: INTERNATIONAL CONFERENCE ON PRECISION AGRICULTURE, 17., CONGRESSO BRASILEIRO DE AGRICULTURA DE PRECISÃO DIGITAL, 11., 2026, Porto Alegre, RS. Anais...Porto Alegre: AsBraAP; AP&D, de 13 a 16 de julho de 2026.
Descripción:	Yield maps are fundamental tools in precision agriculture, as they allow for the evaluation of whether corrective actions taken before and during the season achieved the intended effects by visualizing production heterogeneity within the cultivation area. However, in fruit farming, the manual nature of harvesting has historically made the generation of thematic maps problematic. In Brazil, initiatives developed by the Federal University of Technology – Paraná (UTFPR) in partnership with Embrapa resulted in national fruit harvest mapping systems, contributing to the adaptation of this technology to local conditions and expanding its application across different production chains. Nevertheless, early implementations required a partially static workflow, relying on fixed collection points within the orchard. This did not accommodate practices used in large-scale operations, where bins for unloading harvest bags are itinerant, moved by tractors and trailers. Given this context, the objective of this experiment was to evaluate methods for processing harvest data and generating thematic maps using non-fixed unloading points. The advancement toward an itinerant harvest data collection system enabled the broader application of yield maps to different fruit harvesting systems. Furthermore, the algorithm created for generating thematic maps of itinerant fruit harvesting can be applied in automated precision agriculture tools such as AgDataBox-Map (ADB-Map)—a web-based solution focused on the analysis and visualization of agricultural spatial data, with an emphasis on using yield maps for management support. The algorithm was applied in an experiment conducted in partnership with the company RASIP in apple orchards, utilizing an innovative data collection system during harvest with itinerant unloading points. Harvest workers operated along the rows carrying bags equipped with sensors capable of recording bag identification and GPS position every six seconds, among other relevant information. These bags, which were previously unloaded into fixed bins positioned within the orchard, are now unloaded into bins transported continuously by tractor-pulled trailers (itinerant bins). This shift from a static to an itinerant system required the recalculation of bin location centroids during map generation. The data obtained were processed via an algorithm developed in Python, to be implemented in AgDataBox-Map in the future, enabling the generation of detailed yield maps and their use in delineating result-based management zones. The results demonstrate that the algorithm is a practical and efficient tool for transforming harvest data into useful spatial information, contributing to more precise and sustainable decisions in fruit farming.
NAL Thesaurus:	Precision agriculture Decision making
Palabras clave:	Yield maps Fruit farming Management zones
Tipo de Material:	Artigo em anais e proceedings
Acceso:	openAccess
Aparece en las colecciones:	Artigo em anais de congresso (CNPUV)