Influência da altura das garras dos pneus de um trator em área de plantio direto

This work was carried out to evaluate the performance of a farm tractor fitted with two sets of tires with high lugs and another set of tires with tallow lugs in straw without tillage (corn straw). The travel speeds used were approximately 4, 5, 6 and 7 km h(-1) and a constant pulling force of 25 kN was fixed. Tractor traction, forward speed, slip and consumption of fuel were measured and drawbar power, the ratio between the consumption and power and traction coefficient were calculated. It was observed that the tractor performance was similar to high and low lug tire conditions, in an area covered with corn straw.

Desempenho de trator agrícola em três superfícies de solo e quatro velocidades de deslocamento

The aim of this work was to evaluate tractor performance in soil with three different surfaces: firm soil without covering, mobilized soil, and firm soil with vegetal residue of corn and braquiaria, for four forward speeds. The experiment was accomplished in three plots determined by the soil conditions. In each plot four treatments were composed by the forward speed obtained by the changes of the tractor gear. Six repetitions were used in each plot, totaling 72 experimental units, combination of the traction, slip of the rear and front wheels, forward speed and fuel consumption. The values of the tractor performance obtained led to the conclusion that in the firm soil without vegetable covering the tractor performance was better, followed by the soil with the firm surface and covered with corn straw and braquiaria and finally the mobilized soil. The best tractor performance was obtained in the C1 gear that supplied the forward speed of 6 km h(-1).

Avaliação do ruído e da vibração no posto de trabalho em dois tratores agrícolas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Desenvolvimento e avaliação de equipamentos e metodologia para determinação de parâmetros físicos do solo relacionados a dias trabalháveis com máquinas agrícolas

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Limitações humanas do operador de máquinas agrícolas e o ambiente socio-laboral na sojicultura

Pós-graduação em Agronomia (Ciência do Solo) - FCAV

Avaliação dos níveis de vibração no operador de um trator agrícola ensaiado em pista normatizada em diferentes velocidades de deslocamento

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Avaliação dos níveis de vibração no operador de um trator agrícola ensaiado em pista normatizada em diferentes velocidades de deslocamento

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Bringing the farmer perspective to agricultural robots

The research reported in this paper explores autonomous technologies for agricultural farming application and is focused on the development of multiple-cooperative agricultural robots (AgBots). These are highly autonomous, small, lightweight, and unmanned machines that operate cooperatively (as opposed to a traditional single heavy machine) and are suited to work on broadacre land (large-scale crop operations on land parcels greater than 4,000m2). Since this is a new, and potentially disruptive technology, little is yet known about farmer attitudes towards robots, how robots might be incorporated into current farming practice, and how best to marry the capability of the robot with the work of the farmer. This paper reports preliminary insights (with a focus on farmer-robot control) gathered from field visits and contextual interviews with farmers, and contributes knowledge that will enable further work toward the design and application of agricultural robotics.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Requirements for information management system in agricultural contracting

Menneinä vuosikymmeninä maatalouden työt ovat ensin koneellistuneet voimakkaasti ja sittemmin mukaan on tullut automaatio. Nykyään koneiden kokoa suurentamalla ei enää saada tuottavuutta nostettua merkittävästi, vaan työn tehostaminen täytyy tehdä olemassa olevien resurssien käyttöä tehostamalla. Tässä työssä tarkastelun kohteena on ajosilppuriketju nurmisäilörehun korjuussa. Säilörehun korjuun intensiivisyys ja koneyksiköiden runsas määrä ovat työnjohdon kannalta vaativa yhdistelmä. Työn tavoitteena oli selvittää vaatimuksia maatalouden urakoinnin tueksi kehitettävälle tiedonhallintajärjestelmälle. Tutkimusta varten haastateltiin yhteensä 12 urakoitsijaa tai yhteistyötä tekevää viljelijää. Tutkimuksen perusteella urakoitsijoilla on tarvetta tietojärjestelmille.Luonnollisesti urakoinnin laajuus ja järjestelyt vaikuttavat asiaan. Tutkimuksen perusteella keskeisimpiä vaatimuksia tiedonhallinnalle ovat: • mahdollisimman laaja, yksityiskohtainen ja automaattinen tiedon keruu tehtävästä työstä • karttapohjaisuus, kuljettajien opastus kohteisiin • asiakasrekisteri, työn tilaus sähköisesti • tarjouspyyntöpohjat, hintalaskurit • luotettavuus, tiedon säilyvyys • sovellettavuus monenlaisiin töihin • yhteensopivuus muiden järjestelmien kanssa Kehitettävän järjestelmän tulisi siis tutkimuksen perusteella sisältää seuraavia osia: helppokäyttöinen suunnittelu/asiakasrekisterityökalu, toimintoja koneiden seurantaan, opastukseen ja johtamiseen, työnaikainen tiedonkeruu sekä kerätyn tiedon käsittelytoimintoja. Kaikki käyttäjät eivät kuitenkaan tarvitse kaikkia toimintoja, joten urakoitsijan on voitava valita tarvitsemansa osat ja mahdollisesti lisätä toimintoja myöhemmin. Tiukoissa taloudellisissa ja ajallisissa raameissa toimivat urakoitsijat ovat vaativia asiakkaita, joiden käyttämän tekniikan tulee olla toimivaa ja luotettavaa. Toisaalta inhimillisiä virheitä sattuu kokeneillekin, joten hyvällä tietojärjestelmällä työstä tulee helpompaa ja tehokkaampaa.

Using spectral information and machine vision for bruise detection on peaches and apricots

R. Zwiggelaar, Q. Yang, E. Garcia-Pardo and C.R. Bull, 'Using spectral information and machine vision for bruise detection on peaches and apricots', Journal of Agricultural Engineering Research 63 (4), 323-332 1996)

Soil compaction by machine traffic and least limiting water range related to soybean yield

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Seeder-fertilizer machine: energetic demand as a function of vegetal covering handling and manure deposition shank depth in no-tillage system

O trabalho teve por objetivo avaliar a demanda energética de uma semeadora-adubadora, em função do tipo e manejo da cultura de cobertura vegetal e da profundidade da haste de deposição de adubo. Foi utilizado um trator Valtra BM100, instrumentado, para tracionar uma semeadora-adubadora de precisão equipada com quatro fileiras de semeadura espaçadas de 0,9 m para cultura de milho. O experimento foi conduzido em parcelas subsubdivididas, na área experimental do Laboratório de Máquinas e Mecanização Agrícola (LAMMA) da UNESP-Jaboticabal, utilizando duas culturas de cobertura (mucuna-preta e crotalária), três manejos dessas coberturas, sendo dois mecânicos (triturador de palhas e rolo-faca) e um químico (pulverização com herbicida), realizados 120 dias após a semeadura das culturas de cobertura e três profundidades da haste de deposição do adubo (0,11; 0,14 e 0,17 m), perfazendo 18 tratamentos, com quatro repetições, totalizando 72 observações. Foram avaliados os parâmetros velocidade de deslocamento, patinagem, força na barra de tração, força de pico, potência na barra de tração, potência de pico e consumo de combustível. Pôde-se concluir que a força na barra de tração foi menor para as profundidades de 0,11 e 0,14 m da haste sulcadora de adubo, o mesmo ocorrendo para força de pico, potência na barra de tração e consumo volumétrico. O consumo específico foi menor na profundidade de 0,17 m da haste sulcadora de adubo. As culturas de cobertura e seus manejos não interferiram no desempenho das máquinas estudadas.

Networked control system for the guidance of a four-wheel steering agricultural robotic platform

A current trend in the agricultural area is the development of mobile robots and autonomous vehicles for precision agriculture (PA). One of the major challenges in the design of these robots is the development of the electronic architecture for the control of the devices. In a joint project among research institutions and a private company in Brazil a multifunctional robotic platform for information acquisition in PA is being designed. This platform has as main characteristics four-wheel propulsion and independent steering, adjustable width, span of 1,80m in height, diesel engine, hydraulic system, and a CAN-based networked control system (NCS). This paper presents a NCS solution for the platform guidance by the four-wheel hydraulic steering distributed control. The control strategy, centered on the robot manipulators control theory, is based on the difference between the desired and actual position and considering the angular speed of the wheels. The results demonstrate that the NCS was simple and efficient, providing suitable steering performance for the platform guidance. Even though the simplicity of the NCS solution developed, it also overcame some verified control challenges in the robot guidance system design such as the hydraulic system delay, nonlinearities in the steering actuators, and inertia in the steering system due the friction of different terrains. Copyright © 2012 Eduardo Pacincia Godoy et al.