Characterization of roller and Ferkar-milled pulse flours using laser diffraction and scanning electron microscopy

Sivakumar, Chitra; Chaudhry, Muhammad Mudassir Arif; Nadimi, Mohammad; Paliwal, Jitendra; Courcelles, Janelle

doi:10.1016/j.powtec.2022.117803

Cited by 24 publications

(14 citation statements)

References 26 publications

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“…However, such a model demands more classes to be comprehended. Another interesting topic for future research is the performance of similar state-of-the-art deep learning models in robotic systems for other applications such as grading agri-food products [ 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ], monitoring crop diseases [ 59 , 60 , 61 ], and assessing crop growth and yield [ 62 , 63 , 64 , 65 , 66 ].…”

Section: Resultsmentioning

confidence: 99%

Detection of Coconut Clusters Based on Occlusion Condition Using Attention-Guided Faster R-CNN for Robotic Harvesting

Divyanth

Soni

Pareek

et al. 2022

Foods

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Manual harvesting of coconuts is a highly risky and skill-demanding operation, and the population of people involved in coconut tree climbing has been steadily decreasing. Hence, with the evolution of tree-climbing robots and robotic end-effectors, the development of autonomous coconut harvesters with the help of machine vision technologies is of great interest to farmers. However, coconuts are very hard and experience high occlusions on the tree. Hence, accurate detection of coconut clusters based on their occlusion condition is necessary to plan the motion of the robotic end-effector. This study proposes a deep learning-based object detection Faster Regional-Convolutional Neural Network (Faster R-CNN) model to detect coconut clusters as non-occluded and leaf-occluded bunches. To improve identification accuracy, an attention mechanism was introduced into the Faster R-CNN model. The image dataset was acquired from a commercial coconut plantation during daylight under natural lighting conditions using a handheld digital single-lens reflex camera. The proposed model was trained, validated, and tested on 900 manually acquired and augmented images of tree crowns under different illumination conditions, backgrounds, and coconut varieties. On the test dataset, the overall mean average precision (mAP) and weighted mean intersection over union (wmIoU) attained by the model were 0.886 and 0.827, respectively, with average precision for detecting non-occluded and leaf-occluded coconut clusters as 0.912 and 0.883, respectively. The encouraging results provide the base to develop a complete vision system to determine the harvesting strategy and locate the cutting position on the coconut cluster.

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Section: Resultsmentioning

confidence: 99%

Detection of Coconut Clusters Based on Occlusion Condition Using Attention-Guided Faster R-CNN for Robotic Harvesting

Divyanth

Soni

Pareek

et al. 2022

Foods

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“…Various forces applied to grains and/or legumes during milling fragment their starch granules into smaller particles. Factors affecting the degree of starch damage include quality and variety of seeds, flour fineness or coarseness, pretreatment, and mill type and operational settings (Sivakumar, Chaudhry, Nadimi, et al, 2022;Thakur et al (2019); Wang et al (2020)). Maskus et al (2016a) ((reported that the roller mill had the most starch damage compared to stone and hammer mills for the whole and split yellow pea flours.…”

Section: Starch Damagementioning

confidence: 99%

“…Various forces applied to grains and/or legumes during milling fragment their starch granules into smaller particles. Factors affecting the degree of starch damage include quality and variety of seeds, flour fineness or coarseness, pretreatment, and mill type and operational settings (Sivakumar, Chaudhry, Nadimi, et al., 2022; Sivakumar, Chaudhry, Paliwal, et al., 2022; Thakur et al (2019); Wang et al (2020)). Maskus et al.…”

Section: Important Pulse Properties From a Milling/processing Perspec...mentioning

confidence: 99%

Non‐destructive characterization of pulse flours—A review

Sivakumar

Findlay

Karunakaran

et al. 2023

Comp Rev Food Sci Food Safe

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The consumption of plant-based proteins sourced from pulses is sustainable from the perspective of agriculture, environment, food security, and nutrition. Increased incorporation of high-quality pulse ingredients into foods such as pasta and baked goods is poised to produce refined food products to satisfy consumer demand. However, a better understanding of pulse milling processes is required to optimize the blending of pulse flours with wheat flour and other traditional ingredients. A thorough review of the state-of-the-art on pulse flour quality characterization reveals that research is required to elucidate the relationships between the micro-and nanoscale structures of these flours and their millingdependent properties, such as hydration, starch and protein quality, components separation, and particle size distribution. With advances in synchrotron-enabled material characterization techniques, there exist a few options that have the potential to fill knowledge gaps. To this end, we conducted a comprehensive review of four high-resolution nondestructive techniques (i.e., scanning electron microscopy, synchrotron X-ray microtomography, synchrotron small-angle X-ray scattering, and Fourier-transformed infrared spectromicroscopy) and a comparison of their suitability for characterizing pulse flours. Our detailed synthesis of the literature concludes that a multimodal approach to fully characterize pulse flours will be vital to predicting their end-use suitability. A holistic characterization will help optimize and standardize the milling methods, pretreatments, and post-processing of pulse flours. Millers/processors will benefit by having a range of well-understood pulse flour fractions to incorporate into food formulations.

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“…The potential of deep-learning algorithms has been demonstrated in almost all stages of agricultural activities, paving the way for efficient handling and non-destructive evaluation [1][2][3][4][5][6][7]. One of the agricultural domains that could benefit from these algorithms is weed management.…”

Section: Introductionmentioning

confidence: 99%

Image-to-Image Translation-Based Data Augmentation for Improving Crop/Weed Classification Models for Precision Agriculture Applications

et al. 2022

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Applications of deep-learning models in machine visions for crop/weed identification have remarkably upgraded the authenticity of precise weed management. However, compelling data are required to obtain the desired result from this highly data-driven operation. This study aims to curtail the effort needed to prepare very large image datasets by creating artificial images of maize (Zea mays) and four common weeds (i.e., Charlock, Fat Hen, Shepherd’s Purse, and small-flowered Cranesbill) through conditional Generative Adversarial Networks (cGANs). The fidelity of these synthetic images was tested through t-distributed stochastic neighbor embedding (t-SNE) visualization plots of real and artificial images of each class. The reliability of this method as a data augmentation technique was validated through classification results based on the transfer learning of a pre-defined convolutional neural network (CNN) architecture—the AlexNet; the feature extraction method came from the deepest pooling layer of the same network. Machine learning models based on a support vector machine (SVM) and linear discriminant analysis (LDA) were trained using these feature vectors. The F1 scores of the transfer learning model increased from 0.97 to 0.99, when additionally supported by an artificial dataset. Similarly, in the case of the feature extraction technique, the classification F1-scores increased from 0.93 to 0.96 for SVM and from 0.94 to 0.96 for the LDA model. The results show that image augmentation using generative adversarial networks (GANs) can improve the performance of crop/weed classification models with the added advantage of reduced time and manpower. Furthermore, it has demonstrated that generative networks could be a great tool for deep-learning applications in agriculture.

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Characterization of roller and Ferkar-milled pulse flours using laser diffraction and scanning electron microscopy

Cited by 24 publications

References 26 publications

Detection of Coconut Clusters Based on Occlusion Condition Using Attention-Guided Faster R-CNN for Robotic Harvesting

Detection of Coconut Clusters Based on Occlusion Condition Using Attention-Guided Faster R-CNN for Robotic Harvesting

Non‐destructive characterization of pulse flours—A review

Image-to-Image Translation-Based Data Augmentation for Improving Crop/Weed Classification Models for Precision Agriculture Applications

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