Assessment of mechanical damage to flaxseeds using radiographic imaging and tomography

Nadimi, Mohammad; Loewen, Georgia; Paliwal, Jitendra

doi:10.1016/j.atech.2022.100057

Cited by 21 publications

(27 citation statements)

References 18 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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“…The authors believe that the proposed methodology can revolutionize intelligent crop/weed classifiers. An interesting topic for future work could be to examine the capability of the proposed approach on other machine vision-based applications, such as fruit maturity detection [69,70], fruit grading [71], agri-food product microstructural evaluation [65,72,73], crop disease identification [74], and crop growth and yield monitoring [75][76][77].…”

Section: Performance Analysis Of Transfer Learning Methodsmentioning

confidence: 99%

“…The mathematics and computations behind the discriminant analysis and its regularized version can be further explored in [62] and [63]. These classifiers have also been extensively used for classification tasks in precision agriculture applications [56,64,65].…”

Section: Linear Discriminant Analysismentioning

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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“…However, excessively fanning/blowing overly dry gasses of any kind, including ozone, over grain would have the risk of reducing the MC to a point where the seed becomes brittle and susceptible to mechanical breakage. Considering researchers have recently reported that MCs at or below 6% for axseed can make the seeds susceptible to mechanical damage (Nadimi 2022b), the MC of 8.6 % should not have any of such concerns. Considering very high MCs can also lead to spoilage, keeping the seeds at a safe MC is critical.…”

Section: Moisture Content Testingmentioning

confidence: 99%

Advanced oxidative decontamination of flax and its impacts on storage

Findlay

Singh

Nadimi

et al. 2022

Preprint

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The metabolic actions of storage fungi and other microorganisms can cause spoilage and post-harvest losses in agricultural commodities, including flaxseed. These microbial contaminants are oxidized with hydroxyl radicals that are efficiently generated when ozone, hydrogen peroxide (H2O2), and ultraviolet (UV) light react in an advanced oxidative process (AOP). The present work explores what we believe is the first application of an AOP technology to reduce mould on whole brown and yellow flaxseed. The impact of AOP on storage and quality parameters was assessed by measuring the fatty acid value (FAV), germination rate, moisture content (MC), and visible mould growth after 12 weeks of storage. At 30°C and 75% relative humidity (RH), the yellow decontaminated flaxseed showed a 31% decrease in the number of seeds with visible mould without any adverse effect on germination rate, FAV and MC. In contrast, the same AOP treatment created an insignificant decrease in mould in stored brown flaxseed, at the cost of decreasing the germination rate and increasing FAV. Adverse effects of AOP on brown flaxseed were not readily apparent but became measurable after storage. Moreover, Fourier transform infrared (FTIR) spectroscopy was utilized to explore the rationale behind the different reactions of flaxseed varieties to AOP. The corresponding results indicated that the tolerance of yellow flaxseed to AOP might be related to its richness in olefins. The authors believe that technologies that harness advanced oxidative processes open new horizons in decontamination beyond ozone alone and towards increasing the shelf life of various agri-food products.

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Assessment of mechanical damage to flaxseeds using radiographic imaging and tomography

Cited by 21 publications

References 18 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

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

Advanced oxidative decontamination of flax and its impacts on storage

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