Proceedings of the Brazilian Congress of Agroinformatics (SBIAGRO)
Desenvolvimento do aplicativo “Melhor Plantio” para recomendação de calagem e adubação de culturas agrícolas
This meta-paper is about the development and validation of a mobile application for calculating lime and fertilizer requirements for various agricultural crops, called "Melhor Plantio." The work involved four stages, including mathematical development based on state recommendation manuals in Brazil, the creating of the application was using Android Studio and Excel, and conducting tests with students, teachers, and open case studies in the field. The results allowed for the correction of errors and improved the efficiency of the program, which was found to be effective and an important tool for professionals in agricultural sciences and rural producers.
Análise do Percentual de Germinação de Sementes a partir do uso em Aplicação Mobile
The goal of the present work is to discuss the development of a feature for an existing mobile application, in order to analyze the germination percentage of seeds in a laboratory setting. The implemented feature provides researchers the ability to register the data about the amount and type of seed to be studied, also analyzing how the seeds have been behaving in a graphical way. The functionality was tested with data from Poincianella pyramidalis seeds which were previously collected and analyzed in the laboratory, and the outcomes obtained indicated the developed feature to have promising potential, expanding the use of the existing mobile application in the context of seed analysis.
Utilizando Aprendizado de Máquina Explicável para Previsão de Severidade de Ferrugem Asiática da Soja
Asian rust is a disease caused by the fungus Phakopsora pachyrhizi; it has the soybean as one of its hosts, and its appearance depends on multiple enviromental variables. This paper proposes using state-of-the-art machine-learning models for predicting the severity of Asian rust on soybean crops. We combine geological and meteorological data with datasets on soybean crops to obtain a dataset that enables better exploring the capabilities of the models. In particular, we focus on explainable models. The extended dataset, in combination with the models, enabled achieving a good accuracy for prediction of Asian rust severity for several crops in several Brazilian cities.
Desenvolvimento de um aplicativo para o planejamento e estimativa das perdas na colheita de soja
Soybean harvesting is performed mechanically, and this stage has a high hourly operating cost. During harvesting, losses occur due to different factors ranging from poor harvester adjustment to the way it is operated. We propose the development of an application to assist in decision-making in the planning of mechanized soybean harvesting called ColheSoja, with features to perform the planning of mechanized harvesting, as well as to estimate losses and harvesting costs. The application becomes an important tool to assist in decision-making by farmers and field technicians, aiming at the planning and management of mechanized soybean harvesting.
Análise experimental da complexidade do problema da minimização da consanguinidade em sistemas de acasalamento
Animal breeding relies on mating systems to produce the best possible herd’s next generation. Besides expected genetic and economic gain, keeping low inbreeding levels is important to avoid problems related to inbreeding depression. This work aims to begin studying the complexity of that problem through an approach that combines linear programming with branch-and-bound to investigate the average execution time for obtaining the problem solution. The results indicate that the problem seems to be computationally intractable.
Data assimilation in crop models: old experiences in new contexts
Data assimilation has been widely used for improvement of crop models’ estimates, for example to incorporate the effects of external events or compensate calibration errors in large areas. There are then many well-established approaches for those who want to take advantage of satellite imagery and reduce uncertainty or model error. However, its use in different contexts requires exploring aspects of the pipeline that are not as well established, such as which variables to assimilate or how to ascribe uncertainty to observations or model estimates. In this study, we assess the impacts of different noise levels for performing data assimilation in a tomato growth model, with artificial observations of fruit and mature fruit biomass.
Sistema de recomendação de adubação com efluente de esgoto tratado em manejos multicondições para fertirrigação de milho
Sewage treatment from rural areas is precarious in Brazil, causing environmental and health impacts. The biodigester septic tank has shown itself as a solution, producing treated sewage effluent (TSE) that can be reused in fertilization. Some works with TSE in agriculture targets specific conditions, but they do not translate into a generic and algorithmic solution capable of dealing with multiple conditions of cultivation as well as TSE production and quality. In this article, we present a new multi-condition method to TSE reuse in recommendations for corn fertirrigation. The definition of a multicondition flow enabled the development of a responsive application (Web app). The simulations and comparisons carried out resulted in doses similar to the technical recommendations, a bit higher than the literature for nitrogen and slightly lower for potassium and phosphorus. The derived recommendation system constitutes a practical solution for EET reuse in small-scale agriculture.
Estimating peanut invisible losses using machine learning and remote sensing
Monitoring losses of underground crops still needs to be studied, mainly for estimating losses using new techniques that facilitate the farmer's decision-making. This study aimed to estimate peanut invisible losses using remote sensing and machine learning. The experimental area was conducted in a peanut agricultural region near Taquaritinga in São Paulo, Brazil. Seventy sample points were collected, encompassing various aspects of peanut losses, including visible and invisible losses, yield, maturation, and orbital remote sensing data. The statistical analysis used were Principal Components Analysis and Random Forest. The study concluded that the best results were RGB, RE + NIR, NIR + R, and RE to estimate peanut invisible losses.
AgroGraphAPI: uma API para armazenamento e recuperação de dados agrícolas
The evolution of Digital Agriculture requires information systems that meet the needs of production systems’ stakeholders. Application Programming Interfaces (API) are web services that offer functionalities that can be added to other systems, regardless of their building technologies. This work presents an API for efficient agricultural data recording and retrieval, using a graph database as the supporting technology. The text describes the data model, the API’s architecture, the available endpoints, and the expected format of requests. The AgroGraphAPI is useful to anyone interested in maintaining set of collected data without implementing the database’s creation and query procedures.
Peanut Loss Predictions Using Random Forest and Soil Data
This work aimed to evaluate the Random Forest algorithm performance to predict mechanical digging loss of the peanut crop. Four approaches were tested: only soil texture data, soil texture + TPI, soil texture + TWI, and soil texture + TPI + TWI. The model's performance was evaluated regarding precision (coefficient of determination) and accuracy (mean absolute error). The results found in this work proved promising in predicting peanut digging losses. Our models achieve results with an approximate 100 kg ha-1 prediction error. In addition, incorporating one or more topographical indexes in conjunction with soil texture data as features notably improves the models' precision and accuracy.
Sistema de Monitoramento Agrícola utilizando LoRa 433-MHz, IoT e Aplicativo Móvel
The objective of this article is to present the development and application of a data monitoring system for plantations based on IoT (Internet of Things) technology, the LoRa (Long Range) wireless communication protocol, and a mobile application. The system in the plantation consists of a microcontroller and sensors that collect various types of data, which are sent to the central server through the LoRa communication protocol. Subsequently, the data is processed and stored in the cloud-hosted database, where it is presented by the mobile application. The use of the system enables effective real-time management of different areas of the plantation, resulting in resource and time savings.
Desenvolvimento de um controlador de lógica fuzzy para irrigação por gotejamento
Drip irrigation is a technique that can contribute to food security by increasing crop productivity and reducing water consumption. One of the challenges faced by farmers is determining the optimal time and amount of irrigation for each crop and environmental conditions. Inadequate irrigation management can lead to water losses through evaporation or deep percolation, as well as water stress or excess water in plants. In this context, this work proposes a fuzzy logic algorithm for drip irrigation that can adjust the time and irrigation interval according to the crop development phase, soil texture, evapotranspiration and rainfall. The experiments demonstrate that the controller is capable of estimating the irrigation time and interval, considering the input variables defined in the model. The controller contributes to more efficient and sustainable drip irrigation, as it avoids water waste and water stress on plants. This also facilitates management, as it reduces the need for human intervention and provides intelligent feedback for decision making.
Peanut yield estimation models using Machine Learning techniques and Google Earth Engine
The estimation of the yield for the peanut crop can be performed by visual methods, traditional destructive methods, which are time consuming, laborious and with imprecise predictions. Thus, the objective was to use SR techniques and Artificial Neural Networks (ANN) in the development of an innovative method for yield prediction in the peanut crop. The experiments were conducted in the state of São Paulo in the 2021/2022 crop season, in 6 commercial farms in sandy and clayey areas. The RBF and MLP networks were able to estimate peanut yield with an accuracy below 1000 kg/ha. GNDVI was a better vegetation index with estimation accuracy of 238.7 and 296.54 for the RBF and MLP networks, respectively.
Sensoriamento proximal aplicado ao algoritmo SAFER para estimativa de massa de forragem em sistema silvipastoril
Forage mass availability directs herd sizing, based on this, the aim of this study was to evaluate a method to estimate pasture mass in a silvopastoral system, through proximal sensing applied to the Simple Algorithm for Evapotranspiration Retrieving (SAFER). For this, forage collections were carried out, as well as the collection of climatic data and reflectance data with a proximal sensor. The data served as input for SAFER and then regression models were generated between pasture variables and SAFER results. SAFER was able to express forage behavior in a promising way, and allowed the creation of an estimation model with high precision for silvopastoral system.
Rastreabilidade da Cadeia Produtiva do Mel com Blockchain
In recent years, food fraud has become a recurring issue in Brazil and worldwide, causing significant losses to the food industry. Honey is the third most affected food product by fraud, following milk and olive oil. This has serious consequences, such as annual revenue losses, risks to public health, and the weakening of the food market. In this study, blockchain technology is explored as a solution to improve transparency in the food supply chain and prevent fraud. The research investigates how blockchain can be applied to traceability in honey production, with the potential to add value to the product, reduce recall costs, and ensure greater reliability in the beekeeping industry.
Inovação aberta na geração de tecnologias digitais que apoiam o agricultor
This article presents a case study involving Embrapa's RD&I projects that shows how the promotion of open innovation can result in assets that help digitize the agricultural sector and provide farmers with technical information. The projects are aligned with the company's strategies, which rely on an important management tool to guide its efforts and allocate resources: the Embrapa Management System. The results presented according to this instrument illustrate how it was possible to collaborate with the increase of works in partnership with the productive sector.
SMART-MAP: Plugin QGIS para interpolação utilizando Krigagem Ordinária e Machine Learning
Machine Learning (ML) algorithms have been used as an alternative to conventional and geostatistical methods in the digital mapping of soil attributes. However, ML algorithms have many variants that can make their application difficult for end users. To fill this gap, the objective of this work was to develop the Smart-Map plugin, to be used in QGIS. The Ordinary Kriging (OK) geostatistical method and the ML Support Vector Machine (SVM) model were implemented in the plugin to generate interpolated maps based on machine learning and OK. Performance comparisons between OK and SVM were performed for sample grids with 112 sampled points.
Diferenciação e caracterização de municípios produtores de bovinos de corte do Bioma Pampa segundo o Censo Agropecuário 2017
Cattle raising in the gaucho pampa is the activity that most characterizes the region since the colonization period. The expansion of monocultures and pastures with exotic species has caused losses in the biome's biodiversity over the years. The objective of this work was to typify the municipalities that produce beef cattle in the Pampa from special tabulations of the 2017 Agricultural Census using machine learning. Emphasis was given to the technical characteristics of livestock production systems, in order to understand their technological standard. The mapping of different production patterns throughout the territory can support institutional actions that are more appropriate for producers, contributing to the conservation of the biome.
Tipologia de Municípios Produtores de Bovinos de Corte no Bioma Amazônia a partir do Censo Agropecuário 2017
The objective of this paper is to typify and characterize the beef cattle production systems in use in the Amazon using data from the 2017 Agricultural Census, obtained by special tabulations. The use of machine learning techniques proved to be able to achieve the proposed objective. Cluster analysis made it possible to separate 441 municipalities into four groups, according to production characteristics and with an emphasis on the use of technology. Knowing the heterogeneity of the systems practiced by the producers is fundamental for the planning of agricultural research actions, technology transfer and rural development, with a view to increasing productivity and reducing the environmental costs of beef cattle raising.
Aplicação de Internet das Coisas (IOT) e Inteligência Artificial (IA) para gestão de aviários e melhorias do desempenho produtivo: um estudo de caso em produtores de uma cooperativa agroindustrial
A Internet das Coisas (IoT) e a Inteligência Artificial (IA) compreendem inovações tecnológicas conhecidas como tecnologias 4.0 que permitem a fusão dos domínios físicos, digitais e biológicos e que se destacam pela velocidade, amplitude e profundidade de suas aplicações. A aplicação das tecnologias 4.0 se deu inicialmente para a indústria de transformação, mas, atualmente, já vêm sendo adotadas em diversos setores, como é o caso do setor agropecuário. Neste setor, estas tecnologias têm um alto potencial para permitir o desenvolvimento, remodelar significativamente as cadeias de valor e contribuir muito para sistemas alimentares mais produtivos, resilientes e transparentes. No Brasil, há um grande público que ainda não adota ou considera este tipo de tecnologia em seu processo produtivo. No que diz respeito à pecuária, especialmente à cadeia produtiva de frango de corte, identifica-se um desafio na padronização da produção, em especial, quanto à desuniformidade do peso das aves ao final de seu ciclo de engorda, que ocorre devido a fatores ambientais e de manejo. Este artigo tem o propósito de apresentar informações gerais sobre um projeto piloto de adoção de IoT e IA em 15 aviários de produtores rurais associados a uma cooperativa do sul do Brasil, para o monitoramento constante de aves, permitindo tomadas de decisões de manejo mais assertivas, tendo como foco o aumento da qualidade dos frangos produzidos, maior lucratividade, maior controle sanitário dos ambientes de produção e atendimento aos preceitos do bem-estar animal. Ao final do projeto, com a solução, foi possível identificar um aumento de produtividade de 7% e os desafios para a difusão.
Comparação de Safras de Cana-de-açúcar por meio de Perfis Temporais de Índices de Vegetação MODIS da API SATVeg no Ambiente QGIS
Brazil is the world's largest producer of sugarcane and satellite-based crop monitoring has been one of the most strategic fields for the sugarcane business. In this context, this work describes the development and application of a new plugin in the Geographic Information System QGIS, named “ComparaSafras”, that makes it possible to generate temporal profiles from the consumption of data from the vegetation indices of the sensor MODIS through query requests to the API SATVeg, enabling the comparison among successive sugarcane growth seasons.
Imagens de Raios X e YOLOv8 para Avaliação Automatizada, Precisa e Não Destrutiva da Qualidade de Sementes Braquiária (Urochloa brizantha)
Seed quality is crucial in agriculture. Companies that produce seeds are concerned about finding rapid tests that accurately assess seed vigor, and the time it takes to conduct these tests is a limiting factor. Here we apply the YOLOv8 model to quickly, automatically, and non-destructively evaluate the physiological potential of seeds. The proposed method uses radiographic images of brachiaria seeds (Urochloa brizantha) to identify and classify them based on their physiological quality. The YOLOv8 network was trained with a dataset of seed images, and the results demonstrated high accuracy in identifying and classifying the seeds, with mAP50 and mAP50-95 metrics of 90.6% and 90.1%, respectively, for all classes.
Uso de Regras de Associação em Processos Tecnológicos para Produção e Conservação de Forragens
In Brazil, there is a great diversity of livestock production systems on pasture, which plays a fundamental role in the conservation of the rural landscape. By means of the a priori algorithm, patterns of combinations between the technologies developed for the production and conservation of forage were extracted. The systematization allowed a better understanding of the technologies that should be the basis for the development of forage production and maintenance, helping in the process of selection and prioritization of research projects.
Kernel Maps of the Bactrocera carambolae Occurrence in Amapá, Brazil
This work analyzes the spatial distribution of the Bactrocera carambolae occurrence between 2014 and 2019 in Amapá, Brazil. They monitored the quarantine pest present using traps distributed throughout the state. The technicians checked traps every fifteen days, recorded the occurrence, and sent the information to MAPA. To analyze the spatial distribution, we evaluated the degree of randomness of the traps using the non-homogeneous K function. Subsequently, we applied the quartic kernel function on the occurrences and the simulated occurrences, considering the non-homogeneous phenomenon and having the population per census tracts and highways as covariates. The results showed that we could use the method to monitor the pest on a regional scale.
Sistema para Gestão Climática na Produção de Feno
Climate control is essential to determine the quality of crops and agricultural inputs; in this sense, to assist the producer the research proposes the development of a computer system to contribute to the management of informative data regarding the climate, also assisting in decision-making. The proposed solution consists of the development of software for the daily monitoring of the crop dehydration process considering the main variables involved in environmental evaporation, the producer will have access to information aiming at economy and efficiency in the production process. The target audience will be small producers, the dissemination of technology can be done through cooperatives and rural extension.
Governança de dados na agricultura digital: relato da experiência do C4AI
The practical implementation of AI systems in digital agriculture presents organizational challenges due to the different sources, formats, and volumes of data processed for different purposes. The article examines the data governance actions employed in research at the Center for Artificial Intelligence (C4AI) at the University of São Paulo (USP). We discuss the management of datasets through a transdisciplinary collaboration between Agriculture, Computer Science, and Law. We carried out analyzes on the legal privacy and data protection framework, including ISO 27001 and data protection policy of the University of São Paulo. The results include the creation of a committee responsible for developing a data use agreement, privacy and cookies policy, project data management plan, consent form, and training actions aimed at the community of researchers from different areas of C4AI knowledge.
Gestão de Dados de Políticas Públicas: o caso da avaliação de cardápios da Alimentação Escolar usando mineração de textos
The Brazilian School Feeding Program contributes to the improvement and development of schools through the transfer of funds. However, there is no feedback to monitor the menu quality to guide the actions of the program to promote food security and nutrition. In this paper, the construction of a classification method for generating evaluation components from school menu data is presented. All evaluation components were obtained with negligible error rates and in a viable time.
Visão Computacional aplicada na identificação de doenças na fruticultura: uma Revisão Sistemática da Literatura
Models based on computer vision have been effectively employed for the detection and classification of plant diseases. However, the existence of studies and their results are very scattered. This study presents the results of a Systematic Literature Review (SLR) that primarily focused on the application of computer vision to support the identification of pests/diseases in fruit farming. The main contributions of this work lie in describing the most commonly used techniques and tools, along with the indication of the crops and plant structures that serve as the basis for the analyses.
Exploring conditional missing patterns for automated bacteria identification using MALDI-TOF MS data
Training classifiers for automated bacterial identification using MALDI-TOF fingerprints requires addressing class-conditional missingness patterns (CMPs). A CMP is a non-missing-at-random pattern that provides evidence for classification. One possible strategy to handle CMPs is feature stratification. This work evaluated the effectiveness of stratification in training naive Bayes classifiers for the proposed task through two experiments. The first experiment compared the predictive performance of categorical naive Bayes classifiers trained on stratified/discretized features with the performance of a Gaussian naive Bayes fitted on imputed data. The second experiment assessed the impact of class imbalance on the differences in the performance of Gaussian and categorical naive Bayes classifiers. The ANOVA results suggest that feature stratification can induce more accurate classifiers. Correlation analysis shows that class imbalance has a low influence on the difference in the performances of classifiers.
Uma arquitetura de rede neural convolucional para detecção de alvos de desmatamento na Amazônia através de imagens SAR ICEYE
In recent decades, solutions based on artificial satellites for observation of the earth's surface have emerged that help in the detection of deforested areas through predominantly optical technology. However, such an approach is hampered by the typical climatic conditions of the Amazon, where cloudiness makes it difficult to see some regions of interest. The use of satellites equipped with synthetic aperture radar (SAR) sensors is a viable option to overcome this inconvenience, since they are able to pass through obstacles, since they use microwaves to perform the imaging. Recently there has been an increase in the use of artificial intelligence in the task of analyzing these images in order to identify different types of land use and land cover. In this sense, the present work proposes the development of an convolucional neural network archtecture capable of contributing to the monitoring of large areas of the Amazon forest through the use of machine learning techniques in order to automatically detect deforestation patterns in orbital radar images.
Uma Solução Baseada em Blockchain para Assegurar a Qualidade Produtiva da Cafeicultura Brasileira
The Brazilian coffee farming stands as one of the most relevant agricultural activities in the country, making a significant contribution to the Gross Domestic Product and earning Brazil international recognition as one of the leading coffee suppliers worldwide. Thus, this article introduces a prototype solution based on Blockchain and Deep Learning, developed to assess the coffee plant’s leaf health, ensuring the fruit’s quality on a global scale. The proposal is grounded in the concepts of data immutability and security provided by the mentioned Blockchain technology, as well as the growing popularity of Decentralized Applications.
Seleção de bandas espectrais apoiada pela meta-heurística PSO para predição do teor de alumínio de amostras de solo
ERD-IR is a technique that can be employed to make a model of soil nutrient prediction by correlating sample data to the respective reference value obtained by chemical analysis. Such data are organized as attributes of a set of high-dimensional records and making a model from these data involves difficulties that impair its performance. To overcome such difficulties, the use of evolutionary algorithms for Feature Selection has shown to be promising. The aim of this article is to identify, with Particle Swarm Optimization metaheuristic, the relevant wavelengths for predicting the aluminum content of soil samples from the Campos Gerais region. Results suggest that, for this scenario, few iterations and small swarm size provide the best subsets.
PalmaS: Apoio à decisão sobre o manejo integrado de cochonilha de escama na cultura da palma forrageira
This work describes the palmaS application, an agriculture 4.0 tool for the management of scale cochineal infestations in forage palms. palmaS simplifies data recording, provides detailed analyses, and offers personalized guidance, contributing to the protection of plantations, reduction of damages caused by infestations, and improvement of forage production in the Northeastern semiarid region. To evaluate the application, experts in the agricultural field responded to a questionnaire after using palmaS. Based on the technicians' responses, a preliminary positive conclusion was reached, indicating that the application has an appropriate learning curve.
Evaluating multiple regressors for the yield of orange orchards
Accurate fruit yield estimation is crucial for making informed decisions about harvesting, storage and marketing. However, estimating fruit yield can be challenging. Currently, yield estimation relies on labor-intensive manual counting combined with statistical methods. However, computer vision has emerged as a potential alternative by enabling automatic fruit counting, thus simplifying the process. In this paper, we assess the effectiveness of various machine learning regressors for yield forecasting based on fruit detection in images captured within the orchard. Our results indicate that deep forward neural networks outperform the other regressors examined, making them the most effective choice for yield prediction.
Classificação do Desempenho dos Rebanhos de Seleção Nelore por meio de Aprendizado de Máquina
The aims of this work was, through data mining techniques, to classify the animals of two Embrapa herds, according to the genomic DEP rules (DEPg), to identify the main attributes (characteristics) that direct the understanding of the different objectives of selection in both herds. Eight attributes were selected, with greater importance for the analysis of animal classification, in relation to the selection herds. To perform the classification of animals according to the herd, three supervised algorithms were used, seeking to verify which would present the best performance: decision tree (J48), logistic model trees (LMT) and random forest (RF). The most accurate algorithm was the RF, which modeled the data with greater fit and accuracy.
Plataforma PRS para monitoramento de pesagens diárias em pecuária de corte
Beef cattle farming is of great importance for Brazil, being one of the major producers and exporters of beef. The use of digital technologies in this sector collaborates to increase efficiency and to provide information more quickly, creating conditions for better decision-making in livestock production. This work presents the architecture and implementation of a platform for receiving, storing, organizing and presenting daily bovine weighing data. Additionally, the platform integrates with computational models for estimating and forecasting biomass consumption and weight gain. Validation is performed with data from two beef cattle properties.
Desdobramentos da Implementação da Rede GO FAIR Agro Brasil no Biênio 2021-2023
This work aims to present to the brazilian community the efforts and works developed in the 2021-2023 biennium for the implementation of the GO FAIR Agro Brasil Thematic Implementation Network. The inter-institutional actions undertaken are highlighted, as a means to promote the dissemination of knowledge about sharing and reuse of research data in agricultural sciences supported by the FAIR principles. As a result, the work shows the path taken so far and the new products built with broad participation from the communities of Agricultural Sciences and alike.
Aprendizado de Máquina na Carcinicultura: Diagnóstico da Doença da Mancha Branca em Camarões
White spot disease is a viral disease that affects shrimp and can cause serious sustainable and economic impacts on the production of these animals. This study performs the diagnosis of this disease using machine learning techniques. By enabling early diagnosis, producers can act quickly to reduce the impacts of the disease, protecting shrimp health and ensuring high quality production. The results were satisfactory, achieving an accuracy rate of 98.61% through the random forest algorithm. The adopted approach contributes significantly to the success and efficiency of the shrimp farming industry, strengthening the agribusiness of this branch.
Machine Learning for Soil Attribute Prediction: An Effectiveness and Dimensionality Reduction Analysis
Traditional soil fertility analyzes are laborious, expensive, timeconsuming and produce hazardous waste. Although many works using machine learning (ML) has been done to address these issues, some algorithms and dimensionality reduction strategies require further investigation. Therefore, in this study we evaluated the potential of Support Vector Regression and Ridge regression in determining soil attributes, and compared principal components regression and partial least squares regression (PLSR). The results showed that Ridge was the most effective model. In addition, our experiments revealed that PLSR was able to achieve statistically equivalent results, and in some cases superior to the baseline, but using a much smaller average number of components.
Classificação de predadores de abelhas melíferas utilizando redes neurais convolucionais e transferência de conhecimento
The preservation of biodiversity is a crucial issue in a world increasingly threatened by environmental changes and the loss of natural habitats. In this context, beekeeping and bee conservation have proven to be essential for the pollination of various agricultural crops and for the maintenance of ecosystems. Bees are important pollinating insects for agriculture and the environment. However, they are also vulnerable to predators such as ants, wasps and flies. Identifying bee predators is important to protect hives and ensure successful pollination. In this article, we present a computational model based on artificial intelligence (AI) to differentiate bees from predators, where it is trained from a database of images of bees and predators. The model uses different feature extractors based on convolutional neural networks (CNNs) to extract features from the images, such features are then classified by different pattern classifiers, such as support vector machine (SVM), K-Nearest Neighbors (k-NN) and multilayer neural network (MLP). The analysis of the experimental results showed that the proposed model obtained a satisfactory accuracy in relation to the Acucária metric, obtaining a value of 86.94%.
CNN aplicada às imagens CBERS-4A e PlanetScope para identificação e mapeamento de plantas daninhas em áreas de plantio com cana-de-açúcar
The Alagoas sugarcane sector has gone from rudimentary to highly computerized. This paper aimed to apply remote sensing data and CNN-based algorithms to identify weed plants in sugarcane areas in the state of Alagoas. The results showed high accuracy for training, but for classification it will be necessary to adapt to the AnalyticMS_SR (PlanetScope) images. One of the ways to improve these results is to improve the obtaining of new samples for the classes that presented confusions and a new training for extraction and use of new weights.
Proposta de uma Plataforma para o Desenvolvimento e Análise Visual de Modelos Preditivos para Doenças da Soja
Models for predicting behavior in the agronomic field have an essential role in helping producers make decisions. Models enable predictions of unfavorable situations and management improvements to reduce costs and increase productivity. In this work, we propose a software architecture to support the development of models and related applications, offering functions to capture, prepare and process agrometeorological data. We also present a sample application built on top of the architecture for simulating soybean rust development under different control actions.
AgroTec: Um sistema de Gestão Pecuária para Dispositivos Móveis
This article presents AgroTec, a system for mobile devices that aims to help small and medium farmers in the livestock management of cattle, goats and pigs, thus contributing to a greater and better use of IT benefits in the field. AgroTec allows livestock farmers to use a low-cost identification tag with a QR Code to manage the animals on their properties more efficiently, from birth or purchase, through reproduction, production and ending with death, slaughter or resale.
Um modelo baseado no indivíduo para avaliação de configurações de plantio push and pull no controle do HLB dos citros
Citrus greening (HLB) is a severe and incurable disease that significantly impacts the citrus industry. To mitigate the impact of this disease, new control strategies have been explored, including the manipulation of spatial configurations of citrus plantings with companion plants that attract or repel the HLB insect vector. This study introduces an individual-based model to assess three simulation scenarios in orchards (in silico) with varying spatial configurations: citrus alone, citrus with mango ("push plant"), and citrus with rue ("pull plant"). The findings demonstrate the potential of intercropping rue with citrus as a pesticide-free method to control the epidemic in the field.
Sistema de auxílio à tomada de decisão da área agrícola DSSAT: automatizando o processo de integração de novas versões
This paper presents a software development process in the automation of testing, code integration and verification of the new version of the software, through the technique of Continuous Integration in a Support System for Decision and Transfer in Agrotechnology (DSSAT). For this, several tools and frameworks were used. The results shows the effective automation of the DSSAT versioning process and, consequently, a reduction in the work performed by its administrators were noticed.
Suavização de Oscilações em Barra de Pulverização com Input Shapping e Controle Adaptativo Robusto por Modelo de Referência
The need for the sprayer boom to be parallel to the ground during operation requires active suspension systems with controllers that minimize oscillations associated with rolling movement. This work proposes the integration of the zero-vibration (ZV) input shaping technique with model-based robust adaptive controllers, the MRAC and the VS-MRAC, comparing their results. The robustness of VS-MRAC is evidenced with gains in tracking and control signal when it is integrated into input shaping.
A GNSS-free navigation strategy for orchards
This paper proposes a GNSS-free strategy allowing a robot to navigate through orchards autonomously. It relies on a perception system made of four cameras arranged to enlarge the robot’s field of view. Thanks to this choice, it becomes possible to perform complete navigation (both alley crossing and headland maneuvers) using only visual data, thus increasing the task’s robustness. Results validate the proposed strategy.
AgriSense: Um Hardware Livre e de Baixo Custo para Agricultura Digital
Future agricultural systems must increase productivity and sustainability in food production and supply. To achieve this, efficiency and accuracy in sensing, processing, sharing, and utilizing agricultural data from various sources are essential. This article introduces AgriSense: a low-cost hardware for digital agriculture. AgriSense features a low-cost Printed Circuit Board (PCB) (approximately US$ 50) with a low-power STM32 microcontroller (19 mA during operation and 6 mA in stand-by mode) and precise sensors for temperature, air humidity, atmospheric pressure, ultraviolet light, soil moisture and pH, carbon dioxide (CO2), and volatile organic compounds (VOCs). AgriSense is an open-source hardware project, aligned with the current agenda of Open Science, and its schematics and images are freely available at public repository.
Proposta de uma arquitetura para gerenciamento e segurança dos dados de lotes da extração do cumarú por meio de um aplicativo mobile e blockchain
The Amazonia 4.0 concept aims to establish an environment that enhances and at the same time protects the Amazon ecosystem. Through research and the use of information and communication technologies (ICTs), the aim is to empower Amazonian populations, enabling them to act as maintainers of this ecosystem. Therefore, in this article, we aim to develop a mobile application that offers traceability of the cumarú production chain. As a way of providing a layer of security, we carried out an initial study of the disruptive technology called blockchain.
Desenvolvimento de um Sistema de Monitoramento Ambiental e Agrícola Utilizando Arduino e XBee
This article presents the development of an environmental and agricultural monitoring system using Arduino and XBee. The system utilizes sensors to collect data on temperature, soil moisture, luminosity, and air humidity, which are transmitted to a base station via the XBee module. The data is processed and visualized in a monitoring application, enabling farmers to access real-time information. The proposed system offers an easy-to-implement, and scalable solution for environmental and agricultural monitoring.
Automatic Segmentation of the Self-organizing Map to Support Territorial Zoning
This paper proposes an algorithm for analyzing clusters of thematic maps with ordinal categorical classes to support territorial zoning. The proposed method combines the Self-Organizing Map with graph segmentation techniques for data clustering. The approach was evaluated with synthetic data and applied to the environmental zoning of the Alto Taquari basin, MS/MT. The results showed the ability of the algorithm to separate the data into unimodal differentiable groups.
Curadoria de conteúdo agrícola disponível em livros digitais
This article shows the importance of digital curation in making open data available and in the dissemination of technical information, dealing especially with the analysis and editing phase of digital books content. The text treatment carried out in the books of the 500 Questions 500 Answers collection for their availability in data repositories illustrates how human work is essential in digital curation in order to achieve better quality of data that are the foundation of digital solutions.
Segmentação por Instâncias de Estágios de Desenvolvimento de Cochonilha de Escama com Mask R-CNN
This work presents preliminary results of a recognition, classification and counting system for scale cochineal (Diaspis echinocacti) in cactus pear samples, with the objective of predicting first and third instar stages of the cochineal, in addition to identifying males and females. Machine learning algorithms and the Mask R-CNN model, image processing techniques, and the use of technologies such as cloud computing were used. There are several challenges along the way, such as data labeling, demand for high computational processing power, with that there is room for improvement, but the model has learned the patterns well, and there is great progress.
Comparando a Eficiência de Redes Neurais Convolutivas para Detecção de Frutos em Pomares de Laranja
Given the importance of citrus farming in Brazil's economy, one of the global powers in this sector, it is essential to constantly renew cultivation techniques to make production more efficient. This study tests the efficiency of the YOLOv8 neural network for object detection, with the purpose of obtaining a more accurate orange crop estimate. The overall performance presented by the network was satisfactory, but it did not allow us to demonstrate that it is better than its previous version YOLOv5 in all metrics.
Sistema de baixo custo para determinação de umidade e massa específica aparente de grãos
This study aimed to develop an affordable hectoliter moisture and weight analyzer suitable for small-scale farmers. The equipment was calibrated for two grain types, namely corn and beans, enabling the determination of moisture content. The moisture levels ranged from 6.18 to 11.08% for corn and from 8.21 to 13.75% for beans, as compared to the oven method. In terms of hectoliter weight determination, the analysis yielded an r² value of 0.928 for beans and 0.926 for corn, when compared to the GEHAKA hectoliter weight kit. The production cost of approximately 595 reais ensures accessibility and feasibility for small-scale agricultural implementation.
Amigo Agricultor: aplicativo móvel de orientação para plantio agrícola
This paper presents the development and functionalities of the mobile application "Amigo Agricultor," which aims to assist farmers and agricultural establishments in selecting the most suitable crops based on variables such as geographic location, weather forecasts, climatic data, soil type, and seasonal orientation. The app was designed to be highly inclusive, employing visuals and auditives resources to communicate with users and provide all the necessary information for successful planting. The "Amigo Agricultor" app seeks to increase agricultural yields and empower farmers by offering user-friendly guidance tailored to their specific needs.
Reconhecimento de padrões de produtividade de milho com a abordagem few-shot learning
Estimating yield is of particular interest in agriculture, and machine learning (ML) techniques have contributed to making it happen in a faster and more efficient way. Considering the frequent difficulty in acquiring large-scale agricultural data, few-shot learning (FSL) methods have emerged as an alternative. The goal of this work was to evaluate the use of different composition methods of images acquired by Remotely Piloted Aircraft (RPA), associated or not with plant height, for classifying maize yield, using classical ML techniques and FSL approach. The results of using FSL showed that the model with a siamese network can be viable without using the average height of the plant.