Influence of Operational Conditions of Mechanized Harvesting on Sugarcane Losses and Impurities

Martins, Murilo Battistuzzi; Ruiz, Daniel

doi:10.1590/1809-4430-eng.agric.v40n3p352-355/2020

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…However, increasing the speed can affect the cutting tools' performance. Martins and Ruiz [11], evaluating raw material losses in sugarcane crops, found no differences for this parameter as a function of operational speed. Marques Filho et al [13] showed that harvest speed could increase raw material losses.…”

Section: Resultsmentioning

confidence: 92%

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Continuous and Impact Cutting in Mechanized Sugarcane Harvest: Quality, Losses and Impurities

et al. 2023

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Sugarcane harvesting requires improvements, particularly in cutting tools. Continuous cutting saws have been introduced as a solution to this issue. This study evaluates the performance of two basal sugarcane cutting systems in different fields: a traditional impact cut system (ICS) with knives and a continuous cut system (CCS) with saw blades. Tests were conducted during two crop cycles in three areas, using a 3 × 2 factorial design with two cutting devices and four replications per treatment. Cut quality indices and ratoon damage were analyzed using descriptive statistics. Raw material losses were subjected to the Shapiro–Wilk normality test, ANOVA, and Tukey’s test at 5% probability. Significant differences in cutting quality were found across different areas. The total crop productivity influenced sugarcane cut quality, with the CCS showing (0.8 Mg ha−1) visible losses in higher productivity areas, which is a 74% increase compared to the ICS. In lower productivity areas, the CCS demonstrated better loss performance (0.8 Mg ha−1). Additionally, the stumps damage rate for the CCS was lower than that for the ICS (0.15 and 0.28, respectively), indicating that saws can preserve cane fields and enhance longevity.

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Section: Resultsmentioning

confidence: 92%

“…The base cutter is a vital sugarcane harvester component. Its work efficiency was directly related to production cost per unit area and its decisive impact on sugarcane yield in the following year [10][11][12]. During mechanized harvesting, the stalk's sugarcane base is cut by two rotating disks equipped with cutting knives.…”

Section: Introductionmentioning

confidence: 99%

Continuous and Impact Cutting in Mechanized Sugarcane Harvest: Quality, Losses and Impurities

et al. 2023

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“…Furthermore, when the mechanical harvester operates smoothly and adheres to specifications, it noticeably decreases the levels of mud, stone, and cane root (Xie et al, 2018). Mechanical removal methods, such as vibration, can often be used to screen out the sand, gravel, and soil (Martins and Ruiz, 2020). However, the top, leaf and cane root are unavoidable impurities as they are naturally part of each sugarcane stem (de Mello et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Detection of breakage and impurity ratios for raw sugarcane based on estimation model and MDSC-DeepLabv3+

Li,

Zhang,

et al. 2023

Front. Plant Sci.

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Broken cane and impurities such as top, leaf in harvested raw sugarcane significantly influence the yield of the sugar manufacturing process. It is crucial to determine the breakage and impurity ratios for assessing the quality and price of raw sugarcane in sugar refineries. However, the traditional manual sampling approach for detecting breakage and impurity ratios suffers from subjectivity, low efficiency, and result discrepancies. To address this problem, a novel approach combining an estimation model and semantic segmentation method for breakage and impurity ratios detection was developed. A machine vision-based image acquisition platform was designed, and custom image and mass datasets of cane, broken cane, top, and leaf were created. For cane, broken cane, top, and leaf, normal fitting of mean surface densities based on pixel information and measured mass was conducted. An estimation model for the mass of each class and the breakage and impurity ratios was established using the mean surface density and pixels. Furthermore, the MDSC-DeepLabv3+ model was developed to accurately and efficiently segment pixels of the four classes of objects. This model integrates improved MobileNetv2, atrous spatial pyramid pooling with deepwise separable convolution and strip pooling module, and coordinate attention mechanism to achieve high segmentation accuracy, deployability, and efficiency simultaneously. Experimental results based on the custom image and mass datasets showed that the estimation model achieved high accuracy for breakage and impurity ratios between estimated and measured value with R2 values of 0.976 and 0.968, respectively. MDSC-DeepLabv3+ outperformed the compared models with mPA and mIoU of 97.55% and 94.84%, respectively. Compared to the baseline DeepLabv3+, MDSC-DeepLabv3+ demonstrated significant improvements in mPA and mIoU and reduced Params, FLOPs, and inference time, making it suitable for deployment on edge devices and real-time inference. The average relative errors of breakage and impurity ratios between estimated and measured values were 11.3% and 6.5%, respectively. Overall, this novel approach enables high-precision, efficient, and intelligent detection of breakage and impurity ratios for raw sugarcane.

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Impurities in Raw Sugarcane Before and After Biorefinery Processing

et al. 2021

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Influence of Operational Conditions of Mechanized Harvesting on Sugarcane Losses and Impurities

Cited by 8 publications

References 10 publications

Continuous and Impact Cutting in Mechanized Sugarcane Harvest: Quality, Losses and Impurities

Continuous and Impact Cutting in Mechanized Sugarcane Harvest: Quality, Losses and Impurities

Detection of breakage and impurity ratios for raw sugarcane based on estimation model and MDSC-DeepLabv3+

Impurities in Raw Sugarcane Before and After Biorefinery Processing

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