The use of image analysis to study the effect of moisture content on the physical properties of grains.

Designing machines and equipment for post-harvest operations of agricultural products requires information about their physical properties. The aim of the work was to evaluate the possibility of introducing a new approach to predict the moisture content in bean and corn seeds based on measuring their dimensions using image analysis using artificial neural networks (ANN). Experimental tests were carried out at three levels of wet basis moisture content of seeds: 9, 13 and 17%. The analysis of the results showed a direct relationship between the wet basis moisture content and the main dimensions of the seeds. Based on the statistical analysis of the seed material, it was shown that the characteristics examined have a normal or close to normal distribution, and the seed material used in the investigation is representative. Furthermore, the use of artificial neural networks to predict the wet basis moisture content of seeds based on changes in their dimensions has an efficiency of 82%. The results obtained from the method used in this work are very promising for predicting the moisture content.

Analysis of Energy Use Efficiency and Greenhouse Gas Emissions of Lemon (Citrus lemon L.) Production in Turkey.

The purpose of this study was to determine the energy use efficiency and greenhouse gas emissions of lemon production. It was performed during the 2019-2020 production period in Turkey. The agricultural inputs and outputs used in lemon production were calculated to determine the energy use efficiency and greenhouse gas emissions. According to study findings, the energy inputs in lemon production were calculated respectively as 16,046.98 MJ ha-1 (55.43%) chemical fertilizers energy, 4168.93 MJ ha-1 (14.40%) chemicals energy, 2815.20 MJ ha-1 (9.72%) electricity energy, 2740.42 MJ ha-1 (9.47%) diesel fuel energy, 1864.80 MJ ha-1 (6.44%) irrigation water energy, 705.67 MJ ha-1 (2.44%) machinery energy and 610.20 MJ ha-1 (2.11%) human labour energy. Total input energy and output energy were calculated as 28,952.20 MJ ha-1 and 60,165.40 MJ ha-1, respectively. Energy use efficiency, specific energy, energy productivity and net energy values were calculated respectively as 2.08, 0.91 MJ kg-1, 1.09 kg MJ-1 and 31,213.20 MJ ha-1. The consumed total energy inputs in lemon production can be categorized as 27.74% direct, 72.26% indirect, 8.55% renewable and 91.45% non-renewable. Total greenhouse gas emissions were calculated as 2650.96 kgCO2­eqha-1 for lemon production, with the greatest share for nitrogen 950.62 kgCO2­eqha-1 (35.86%). Based on the study findings, it was concluded that lemon production in 2019-2020 production season was profitable in terms of energy use efficiency (2.08). Greenhouse gas emission ratio (per kg) was calculated as 0.08. This study is important since there is no study on the energy balance and greenhouse gas emissions in lemon production in Mugla province, Turkey.

Fuzzy modeling of volumetric efficiency and specific energy consumption in agricultural screw conveyors / Modelagem fuzzy de eficiência volumétrica e consumo específico de energia em transportadores agrícolas de parafuso

ABSTRACT: Vertical screw conveyors have low energy efficiency but this is generally acceptable within the normally low power range. Previously, a fuzzy logic approach was used to model volumetric efficiency and specific energy consumption in screw conveyors. The performance of conveyors in different working conditions and the geometry of the screw were studied. It was reported that increasing the screw speed, pitch, and loading angle also increases specific energy consumption. In this study, an intelligent fuzzy model based on the Mamdani approach was developed to predict volumetric efficiency and specific energy consumption. The model inputs included the slope, speed, and pitch of screw conveyors. The fuzzy model consists of 27 rules in which three parameters, namely the goodness of fit (η), relative error (ε), and coefficient of correlation (R), are used to evaluate the model. The goodness of fit, relative error, and coefficient of correlation values were 0.986, 5.28%, and 0.99, respectively, for volumetric efficiency and 0.987, 4.93%, and 0.99, respectively, for specific energy consumption. Results revealed that the developed model is capable of predicting volumetric efficiency and specific energy consumption in barley transport under different working conditions with high accuracy.

RESUMO: Os transportadores de parafuso verticais têm baixa eficiência energética, mas isso geralmente é aceitável dentro da faixa de potência normalmente baixa. Anteriormente, uma abordagem lógica fuzzy era usada para modelar a eficiência volumétrica e o consumo específico de energia em transportadores de parafuso. O desempenho dos transportadores em diferentes condições de trabalho e a geometria do parafuso foram estudados. Verificou-se que aumentar a velocidade do parafuso, a inclinação e o ângulo de carga também aumenta o consumo de energia específico. Neste estudo, um modelo fuzzy inteligente baseado na abordagem de Mamdani é desenvolvido para prever a eficiência volumétrica e o consumo específico de energia. As entradas do modelo incluem a inclinação, velocidade e inclinação dos transportadores de parafuso. O modelo fuzzy consiste em 27 regras, nas quais três parâmetros, a saber, qualidade do ajuste (η), erro relativo (ε) e coeficiente de correlação (R), são usados para avaliar o modelo. Os valores de ajuste, erro relativo e coeficiente de correlação são de 0,986, 5,28% e 0,99, respectivamente, para eficiência volumétrica e 0,987, 4,93% e 0,99, respectivamente, para consumo específico de energia. Os resultados revelam que o modelo desenvolvido é capaz de prever eficiência volumétrica e consumo específico de energia no transporte de cevada sob diferentes condições de trabalho com alta precisão.