The conflict between environmental conservation and agricultural production highlights the need for precise land use and land cover (LULC) mapping to support agro-environmental-related policies. Satellite image time series from the Moderate Resolution Image Spectroradiometer (MODIS) sensor are essential for current LULC mapping efforts. However, most approaches focus on pixel data, and studies exploring object-based spatiotemporal heterogeneity and correlation features in its time series are limited. The objective of this study is to mix the data cube architecture (analysis-ready data—ARD) and the geo-object-oriented time series segmentation via Geographic Object-Based Image Analysis (GEOBIA) to assess its performance in identifying natural vegetation and double-cropping practices over a crop season. The study area was the state of Mato Grosso, Brazil. Results indicate that, by combining GEOBIA and time series analysis (materialized by the multiresolution segmentation algorithm to derive spatiotemporal geo-objects of the MODIS data cube), representative training data collected after a quality control process, and the Support Vector Machine to classify the ARD, the overall accuracy was 0.95 and all users’ and producers’ accuracies were higher than 0.88. By considering the heterogeneity of Mato Grosso’s landscape, the results indicate the potential of the approach to provide accurate mapping.

A banana (Musa spp.) é amplamente consumida globalmente por seu alto valor nutricional. Fatores climáticos, como vento, umidade, precipitação, temperatura e luminosidade, afetam seu desenvolvimento. Para otimizar a produção, é essencial usar tecnologias que monitorem essas variáveis em tempo real. Contudo, pequenos produtores enfrentam limitações financeiras. Este estudo propõe a incorporação de sensores em placas de circuito impresso, permitindo que agricultores obtenham informações essenciais para decisões assertivas e melhorem a produtividade e a qualidade dos produtos.

Anais do Simpósio Internacional em Agronegócio e Desenvolvimento - SIAD (3085-5764) - Sustainable Agroclimatic Module: Innovation For Food Systems In The Face Of Climate Change

Water deficit represents a major environmental constraint that severely limits the growth and yield of common bean (Phaseolus vulgaris L.). Although inoculation with plant growth-promoting rhizobacteria (PGPR) has emerged as a promising strategy to mitigate drought-induced stress, the efficacy of specific strains, such as Priestia aryabhattai CMAA 1363, remains to be fully elucidated. This study evaluated the morpho-agronomic and biochemical responses of common bean to seed inoculation with P. aryabhattai CMAA 1363 under two contrasting irrigation regimes: 100% (well-watered) and 40% (water-restricted) of available water capacity (AWC) under greenhouse conditions. Water restriction significantly compromised plant performance, reducing plant and pod length, root dry biomass, and yield components (pod and grain counts, and total grain mass). Conversely, bacterial inoculation enhanced vegetative traits, increasing plant length by approximately 15% and root dry biomass by approximately 25% compared to non-inoculated controls. Notably, under severe water deficit (40% AWC), inoculated plants achieved a 20% increase in total grain mass per plant relative to their non-inoculated counterparts. Biochemical profiling indicated that inoculation effectively attenuated oxidative stress, as evidenced by lower malondialdehyde (MDA) accumulation and modulated superoxide dismutase (SOD) activity, while water-stressed plants adapted by accumulating total soluble sugars and increasing peroxidase (POD) activity. Overall, P. aryabhattai CMAA 1363 promotes vegetative development, preserves grain production under drought, and orchestrates antioxidant defense mechanisms, highlighting its potential as a sustainable bioinput to improve common bean resilience in water-limited agricultural systems.