Assessment of the genotoxicity of two agricultural residues after processing by diplopods using the Allium cepa assay

Agroindustrial by-products and residues from treatment of sewage sludge have been recently recycled as soil amendments. This study was aimed at assessing toxic potential of biosolid, obtained from a sewage treatment plant (STP), vinasse, a by-product of the sugar cane industry, and a combination of both residues using Allium cepa assay. Bioprocessing of these samples by a terrestrial invertebrate (diplopod Rhinocricus padbergi) was also examined. Bioassay assembly followed standards of the Brazilian legislation for disposal of these residues. After adding residues, 20 diplopods were placed in each terrarium, where they remained for 30 days. Chemical analysis and the A. cepa assay were conducted before and after bioprocessing by diplopods. At the end of the bioassay, there was a decrease in arsenic and mercury. For the remaining metals, accumulation and/or bioavailability varied in all samples but suggested bioprocessing by animals. The A. cepa test revealed genotoxic effects characterized by different chromosome aberrations. Micronuclei and chromosome breaks on meristematic cells and F1 cells with micronuclei were examined to assess mutagenicity of samples. After 30 days, the genotoxic effects were significantly reduced in the soil + biosolid and soil + biosolid + vinasse groups as well as the mutagenic effects in the soil + biosolid + vinasse group. Similar to vermicomposting, bioprocessing of residues by diplopods can be a feasible alternative and used prior to application in crops to improve degraded soils and/or city dumps. Based on our findings, further studies are needed to adequately dispose of these residues in the environment. © 2013 Springer Science+Business Media Dordrecht.

Radionuclide concentrations in raw and purified phosphoric acids from Brazil and their processing wastes: implications for radiation exposures

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

An evaluation into the potential of biological processing for the removal of metals from sewage sludges

The use of sewage sludge in agricultural land as a means of sludge disposal and recycling has been shown to be economical and suitable because of the presence of nutrients such as nitrogen and phosphorus. However, municipal sludges often contain high quantities of toxic metals and other compounds that must be removed for its safe use in agricultural soils. The biological leaching of metals from sewage sludges has been shown to be a promising technique for metal detoxifying in such complex matrix. The process efficiency is dependent on several physico-chemical parameters, such as total solids concentration, metal forms, pH-ORP, and temperature. Scale-up of the process has not yet been defined and is still pursuing the correct operational design. Current research involving the bioleaching of metals from sewage sludge and its application to land, which affects soil physical properties, are presented and discussed.

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

Vibration and tipping of agricultural machinery: A reality to be adjusted

The present study aimed to develop a methodology for the collection, transfer, storage and processing of vibration levels emitted in jobs occupied in agricultural machinery. The reason of this work is the study the vibration dose applied to operators of heavy vehicles and its relation to occupational health, linking the still high number of accidents involving farm machinery in relation to overturning (tipping). There is a need for the development and improvement of efficient tools in measuring vibration and tilt machine work, which minimize damage to health and accident risks for operators.

Greening a Brazilian cotton seed processing company: from challenges to strategic opportunities

This paper aims to shed light on a current and important theme that has been gaining increasing importance for governments, society and companies: the greening of agricultural companies in developing countries. The research objective is to classify a Brazilian cotton seed processing company in the evolutionary stages of environmental management: reactive, preventive or proactive. Design/methodology/approach– A case study was conducted in a Brazilian cotton seed processing company.Findings– The main results are: the studied company is positioned in the preventive stage of environmental management; this company is investing in operational green practices; environmental management in this company is motivated by various factors aiming at an increased competitive advantage. Finally, it can be concluded that this company is looking for green opportunities towards the proactive environmental management stage.