https://www.alice.cnptia.embrapa.br/alice/handle/item/206 Artigo em anais de congresso (CNPDIA) Sat, 25 Jul 2026 03:17:54 GMT 2026-07-25T03:17:54Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186876 Título: Optimizing maize planting density for enhanced economic returns. Autoria: NAIME, J. de M.; SPERANZA, E. A.; LOPES, I. de O. N.; VAZ, C. M. P.; INAMASU, R. Y.; NOGARA NETO, F.; FRANCHINI, J. C. Conteúdo: Abstract: Variable rate seeding (VRS) can enhance profitability, but its adoption by small and medium-sized producers is limited. This work presents a practical on-farm methodology to determine economically optimal corn seeding rates, designed for direct use by farm managers. The methodology was applied in two commercial fields in Paraná State, Brazil. In Field A, four seeding rates were tested on two corn hybrids using a planter with a mechanical drive. In Field B, four management zones (MZs) were delineated using multiple data layers, and variable seeding rates were applied to assess spatially-differentiated responses. Results showed distinct responses between hybrids and MZs. In Field A, increasing the seeding rate by approximately 10% boosted yield by 7.9% for one hybrid but was detrimental to the other. In Field B, an economic analysis revealed that the profit-maximizing seeding rate varied by MZ: a 20% rate increase was optimal in MZ2 (+2.44% profit), while a 10% increase was optimal in MZ4 (+2.57% profit). The study demonstrates that this accessible, on-farm experimental approach allows growers to effectively customize seeding rates, optimizing resource use and profitability without requiring significant investment in specialized technology or expertise. Wed, 01 Jan 2025 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186876 2025-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1181494 Título: A milk-probe to identify the stability condition of the raw milk. Autoria: MELO, W. L. de B.; ZANELA, M. B.; FOSCHINI, M. M.; PERES, R. P. Conteúdo: Abstract: This work concerns an application of the milk probe equipment with the purpose of identifying the stability conditions, whether stable (normal) or unstable (acidic) milk. The milk probe is an instrument composed basically of a light source, LEDs, with 12 different wavelengths. The equivalent absorbance spectrum is obtained through the application of the Kubelka-Munk function. Approximately 537 samples of herd milk were used. The statistical methods were Random Forest, Support Vector Machine (Polynomial SVM) and Logistic Regression in 4095 models of arrangement of wavelengths. The results indicated excellent agreement between the predicted values and those of the classifications. However, these are agreement values, and their precision or accuracy cannot be affirmed. Resumo: Este trabalho trata de uma aplicação do equipamento sondaleite com a finalidade de identificar as condições de estabilidade do leite, se normal ou ácido. O Sondaleite é um instrumento composto basicamente por um fonte de luz, LEDs, com 12 comprimentos de onda distintos. O espectro de absorbância equivalente se obtém através da função de Kubelka-Munk. Cerca de 537 amostras de leites de rebanho foram usadas. O método estatístico foram Random Forest, Support Vector Machine (Polynomial SVM) and Logistic Regression em 4095 modelos de arranjos dos comprimentos de onda. Os resultados indicaram ótimas concordâncias entre os valores previstos e aqueles das classificações. Contudo, trata-se de valor de concordância, não podendo afirmar sua precisão ou acurácia. Wed, 01 Jan 2025 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1181494 2025-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1179144 Título: Evaluation of an IoT System Used with Sensors for the Recognition of Invasive Plants in Groundnut Crops. Autoria: MORENO, B. M.; CRUVINEL, P. E.; COSTA, A. G. F. Conteúdo: Sensors are quite important for data collection from the physical agricultural world, and also a key part of the Internet of Things (IoT) ecosystem. The IoT has enabled monitoring and automation in agriculture, supporting the implementation of precision agriculture applications using sensors in the field. However, the effectiveness of these systems depends on the accurate verification and assessment of network parameters such as connectivity, sensor reliability, and data integrity. Ensuring the proper functioning of IoT devices is crucial to maintaining efficiency, reducing costs, and improving overall agricultural outcomes. This study highlights factors to consider when developing an IoT system based on an experimental field study for pattern recognition of invasive plants in groundnut crops, resulting in classifiers with an accuracy of approximately 80%. Wed, 01 Jan 2025 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1179144 2025-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1179142 Título: Reducing the Dead Zone Time Effect of Actuators in Sensor-Based Agricultural Sprayers under S-shaped Functions Gain Scheduling Management of a Generalized Predictive Control (GPC) Strategy. Autoria: SCHUTZ, D. R.; OLIVEIRA, V. A.; CRUVINEL, P. E. Conteúdo: —This paper presents a study on the relationship between sensors, control systems and actuators for agricultural spraying. Sensors associated with appropriate control systems can be used to support decision-making processes for nozzles in relation to the correct application of pesticides. In such a context, results related to a comparison were evaluated considering not only an adaptive generalized predictive control based on both fuzzy and sigmoid-based strategies for scheduling management but also the enhancement of the dead zone management improving actuators performance in relation to the nozzles stitching’s processes. These systems involving sensors, controllers and switching are essential for the automation of agricultural sprayers, especially for those that work with variable rate application, in management based on precision agriculture. A Sigmoid-based Generalized Predictive Control (SGPC) is proposed for flow rate regulation in agricultural pesticide sprayers. Evaluated against conventional Fuzzy Logic-based GPC (FGPC), the SGPC shows reduced Integral Absolute Error (IAE) and faster rise time despite higher overshoot in certain scenarios. Results indicate enhanced tracking accuracy and dynamic response compared to traditional fuzzy logic approaches. This framework demonstrates potential for improving precision in agricultural spraying systems. Such results can be valuable for the current machinery agricultural industry, which needs to improve productivity and quality gains and reduce negative externalities in favor of food security and sustainability Wed, 01 Jan 2025 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1179142 2025-01-01T00:00:00Z