DSpace Communidade: Programa de Pós Graduação em Ciências Agrárias - Agronomia (PPGCA - AGRO)

QUALIDADE DA PLANTABILIDADE DO MILHO DOCE EM FUNÇÃO DA VELOCIDADE DE SEMEADURA

Fri, 05 Dec 2025 00:00:00 GMT

Título: QUALIDADE DA PLANTABILIDADE DO MILHO DOCE EM FUNÇÃO DA VELOCIDADE DE SEMEADURA Autor(es): Rapaliao, Mariana Joao Pedro Primeiro Orientador: Machado, Tulio de Almeida Primeiro Membro da Banca: Morais, Emmerson Rodrigues de Segundo Membro da Banca: Costa, Fernando Rezende da Abstract: The sowing process, among the production stages, is considered the most important, requiring maximum efficiency. All stages that occurred during sowing were carried out by seeders; these machines are equipped with mechanisms that aim to deposit the seed in the soil and create a favorable environment for its germination. The main objective of this project was to evaluate the plantability quality of sweet corn as a function of different speeds under two seed metering discs. The study was conducted at IF Goiano –Morrinhos Campus located in the municipality of Morrinhos/GO. The design was a randomized block design in a split-plot arrangement. The crop planted was sweet corn. A John Deere 4x2 TDA tractor and a Netz mechanical seeder were used. The operating speeds used were: V1: 2.43; V2: 3.75; V3: 6.00 km h-1. The following variables were evaluated: average linear distance between seeds, quantity of seeds, and type of seed spacing. The results underwent analysis of variance using the F-test at a 5% probability level. The means were subjected to Tukey's test at a 5% probability level; mentioning that the increasing in the sowing speed affects the percentage of spacing. It´s also said that the lowest operating speed provides better seed distribution during sowing. Additionally mentioned, operating at a speed of 6.00 km h⁻¹ (1,67 m s-1), shows the least variability and points within the control limits. Keywords: metering disc, no-till system, types of sieves. Editor: Instituto Federal Goiano Tipo: Dissertação

SISTEMAS INTELIGENTES EMBARCADOS EM DISPOSITIVOS MOVEIS E BASEADOS EM DEEP LEARNING PARA DETECÇÃO E MONITORAMENTO ESPACIAL DE PRAGAS AGRÍCOLAS

Sat, 29 Nov 2025 00:00:00 GMT

Título: SISTEMAS INTELIGENTES EMBARCADOS EM DISPOSITIVOS MOVEIS E BASEADOS EM DEEP LEARNING PARA DETECÇÃO E MONITORAMENTO ESPACIAL DE PRAGAS AGRÍCOLAS Autor(es): Almeida, Guilherme Pires Silva de Primeiro Orientador: Santos, Leonardo Nazário Silva dos Primeiro Membro da Banca: Teixeira, Marconi Batista Segundo Membro da Banca: Oliveira, Mario Anderson de Terceiro Membro da Banca: Morais, Wilker Alves Abstract: Accurate and timely detection of insect pests remains one of the major challenges in modern agriculture, especially in large-scale soybean and maize production systems. Inefficient monitoring practices often result in delayed control interventions and significant yield losses. Recent advancements in deep learning and mobile computing have opened new opportunities for in-field pest identification using lightweight computer vision models. In this context, this thesis presents an integrated framework for intelligent pest detection and spatial monitoring based on deep learning, geostatistical analysis, and mobile applications. First, two datasets of insect pests were constructed and evaluated: a comprehensive high-resolution dataset curated through double-expert validation, and a smaller sample designed for comparative analysis. State-of-the-art detection architectures (YOLO and Detectron2) were trained on both datasets and subsequently converted into TensorFlow Lite (TFLite) and ONNX formats to enable deployment on resource-constrained devices. Even under the least favorable conditions using the reduced dataset and the lightest ONNX model the results reached a precision of 87.3% and accuracy 95.0%, demonstrating the robustness of the pipeline. Building upon these results, a mobile system named AgroInsect was developed. The application performs real-time, on-device detection of four key pest species relevant to Brazilian soybean and maize production (Diabrotica speciosa, Dalbulus maidis, Diceraeus spp., and Spodoptera frugiperda), automatically extracts geolocation metadata, validates spatial consistency based on field boundaries, and synchronizes detections with a cloud database. Spatial visualization is generated through heatmaps and Ordinary Kriging (PyKrige), enabling high-resolution incidence maps. Field evaluations confirmed strong model performance, with overall accuracy of 95.1%, F1-scores above 0.94 for all species, and only 1.1% false detections. The kriging model achieved R² > 0.94 under dense sampling, accurately reproducing ecological spatial patterns. Additionally, this thesis introduces AgroLabIA, a digital platform designed for the storage, annotation, and dissemination of agricultural pest datasets. It provides curated, multi-format datasets suitable for training machine learning models and supports the continuous expansion of new insect and weed classes. The integrated environment that encompasses dataset generation, mobile detection, spatial verification, and geostatistical mapping demonstrate a scalable and operationally robust solution for precision pest monitoring. The results position the AgroInsect database as an effective tool for accelerating decision-making in integrated pest management, particularly in regions with limited connectivity, thus contributing to the consolidation of Agriculture 4.0. Editor: Instituto Federal Goiano Tipo: Tese

EFEITO RESPONSIVO DA CULTURA DO MILHO PELA APLICAÇÃO DE COMPOSTOS MINERAIS

Mon, 23 Jun 2025 00:00:00 GMT

Título: EFEITO RESPONSIVO DA CULTURA DO MILHO PELA APLICAÇÃO DE COMPOSTOS MINERAIS Autor(es): Lora, Jacyr Primeiro Orientador: Soares, Frederico Antonio Loureiro Primeiro Membro da Banca: Cunha, Fernando Nobre Segundo Membro da Banca: Silva, Nelmicio Furtado da Terceiro Membro da Banca: Morais, Wilker Alves Abstract: The use of mineral compounds in agriculture has emerged as a promising strategy to increase crop productivity and sustainability. Many mineral compounds, although not considered fertilizers, stimulate natural processes by providing essential nutrients that participate in various physiological and growth processes, and even improve water use efficiency, tolerance to abiotic stresses, and crop quality. This thesis aimed to evaluate the effects of silicon and zinc fertilization on growth, development, and yield of corn (Zea mays L., grown in Cerrado Red Latosol). Two experiments were carried out in open-air pots using a randomized block design with five doses (0, 25, 50, 100, and 200 kg/ha⁻¹) of potassium silicate and zinc sulfate, respectively, and four replicates. The variables evaluated included morphological, physiological, and productive parameters namely, plant height, stem diameter, number of leaves, leaf length, leaf width, leaf area, leaf mass, stem mass, SPAD index, NDVI index, chlorophyll a, chlorophyll b, total chlorophyll and chlorophyll a/b. The productive components evaluated were straw mass, ear mass, cob mass, ear length, ear diameter, cob diameter, number of rows of grains in the ear, number of grains per row and the mass of 50 grains. In the first experiment, the silicon aplication provided significant increases in plant height, stem diameter, leaf area, chlorophyll content (SPAD and NDVI indices), mass of vegetative organs and ear components, with emphasis on the maximum productivity of 5582.44 kg ha⁻¹ at the dose of 118.31%. The response was predominantly quadratic, indicating an optimum application point for most variables. In the second experiment, zinc also positively influenced the variables studied, especially at intermediate doses. The best results occurred between 100% and 140% of the standard dose, with maximum productivity of 4946.30 kg ha⁻¹ at the dose of 135.56%. Both mineral compounds demonstrated potential to improve the agronomic performance of corn, with silicon being more effective in structural attributes and zinc being more associated with enzymatic metabolism and leaf pigmentation. The data reinforces the importance of rational nutritional management and the need to define adequate application ranges to maximize positive effects and avoid losses due to excess. Editor: Instituto Federal Goiano Tipo: Tese

SISTEMA DE IRRIGAÇÃO INTELIGENTE BASEADO EM LÓGICA FUZZY INTEGRADO COM INTERNET DAS COISAS PARA A CULTURA DO TOMATE CEREJA

Fri, 28 Nov 2025 00:00:00 GMT

Título: SISTEMA DE IRRIGAÇÃO INTELIGENTE BASEADO EM LÓGICA FUZZY INTEGRADO COM INTERNET DAS COISAS PARA A CULTURA DO TOMATE CEREJA Autor(es): Novak, Sergio Souza Primeiro Orientador: Bailão, Adriano Soares de Oliveira Primeiro Membro da Banca: Teixeira, Marconi Batista Segundo Membro da Banca: Ribeiro, Fabiana Girotto Terceiro Membro da Banca: Santos, Charles Barbosa Abstract: This work presents the development, implementation, and experimental validation of an Intelligent Irrigation System using Fuzzy Logic (SILF), integrated with the Internet of Things (IoT), designed to optimize water use in cherry tomato cultivation (Solanum lycopersicum var. cerasiforme). The proposed system addresses the limitations of traditional fixed-time irrigation methods, which often lead to under- or over-irrigation, nutrient leaching, and water waste. The SILF architecture consists of: (i) a sensor network based on the ESP32 microcontroller for monitoring soil moisture, air temperature, and air humidity; (ii) a Python/Flask backend server hosting the fuzzy inference module; and (iii) a web-based interface developed with Angular for remote monitoring and system configuration. The core of the system is a Mamdani-type fuzzy controller, whose membership functions and inference rules were defined based on consultations with a fruit-growing specialist and on the crop’s physiological literature. Every hour, the system processes real-time environmental data, applies 27 fuzzy rules, and—using the centroid defuzzification method—determines the optimal activation time of the hydraulic pump and solenoid valve, ranging from 0 to 60 minutes. Experimental validation was conducted in a protected environment using a randomized block design with a 5 × 3 factorial arrangement, comprising five substrate volumes (3, 6, 9, 12, and 15 L) and three irrigation methods: (i) traditional (30 continuous minutes once per day); (ii) fractionated (6 minutes every 2 hours, totaling 30 minutes per day); and (iii) intelligent irrigation using SILF. A total of 80 cherry tomato plants were evaluated, with drainage (excess water) as the primary response variable. Analysis of variance (ANOVA) revealed highly significant effects (p < 0.0001) of both irrigation method and substrate volume on drainage. The interaction between these factors was not significant (p = 0.3494), indicating the robustness of SILF under different cultivation conditions. Tukey’s multiple comparison test (α = 0.05) demonstrated that SILF resulted in a statistically significant reduction in average drainage compared to traditional fixed-time irrigation methods, while no significant difference was observed between the two fixed-time strategies. Regarding substrate volume, pots with 15 L exhibited significantly lower drainage compared to those with 3 L and 9 L. It is concluded that SILF is a viable and effective technological solution, capable of saving an average of 200 to 300 mL of water per plant per day, corresponding to a potential reduction of approximately 24 L per day for a bench containing 80 pots. The system combines low cost, scalability, remote control, and adaptive decision-making logic that mimics expert reasoning. This work validates the potential of integrating Fuzzy Logic and IoT technologies for precision agriculture, promoting a more sustainable use of water resources. Editor: Instituto Federal Goiano Tipo: Dissertação

DSpace Communidade: Programa de Pós Graduação em Ciências Agrárias - Agronomia (PPGCA - AGRO)

QUALIDADE DA PLANTABILIDADE DO MILHO DOCE EM FUNÇÃO DA VELOCIDADE DE SEMEADURA

SISTEMAS INTELIGENTES EMBARCADOS EM DISPOSITIVOS MOVEIS E BASEADOS EM DEEP LEARNING PARA DETECÇÃO E MONITORAMENTO ESPACIAL DE PRAGAS AGRÍCOLAS

EFEITO RESPONSIVO DA CULTURA DO MILHO PELA APLICAÇÃO DE COMPOSTOS MINERAIS

SISTEMA DE IRRIGAÇÃO INTELIGENTE BASEADO EM LÓGICA​ ​FUZZY INTEGRADO COM INTERNET DAS COISAS PARA A​ ​CULTURA DO TOMATE CEREJA​

SISTEMA DE IRRIGAÇÃO INTELIGENTE BASEADO EM LÓGICA FUZZY INTEGRADO COM INTERNET DAS COISAS PARA A CULTURA DO TOMATE CEREJA