Examination of wheat kernels for the presence of Fusarium damage and mycotoxins using near-infrared hyperspectral imaging

Nadimi, Mohammad; Brown, Jill M.; Morrison, Jason; Paliwal, Jitendra

doi:10.1016/j.meafoo.2021.100011

Cited by 23 publications

(17 citation statements)

References 36 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%

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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“…Another technique called hyperspectral imaging (HSI) combines the advantages of NIR spectroscopy and imaging by simultaneously providing spectral and spatial information about samples. Researchers evidenced the capability of NIR HSI on measuring wheat parameters such as moisture (Fox & Manley, 2014), crude protein (Caporaso et al., 2018a; Mahesh et al., 2015), hardness (Erkinbaev et al., 2019; Mahesh et al., 2015; Zhang et al., 2017), starch content (Fox & Manley, 2014), microbial infection and toxins (Delwiche et al., 2019; Erkinbaev et al., 2022; Femenias et al., 2020a, 2020b; Femenias, Bainotti, et al., 2021; Femenias, Gatius, et al., 2021; Nadimi et al., 2021a; Serranti et al., 2013; Shen et al., 2022), insect infestation (Singh et al., 2009), and mold and sprout damage (Barbedo et al., 2018).…”

Section: Measurement Techniquesmentioning

confidence: 99%

Enhancing traceability of wheat quality through the supply chain

Nadimi

Hawley²,

Liu

et al. 2023

Comp Rev Food Sci Food Safe

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With the growing global population, the need for food is expected to grow tremendously in the next few decades. One of the key tools to address such growing food demand is minimizing grain losses and optimizing food processing operations. Hence, several research studies are underway to reduce grain losses/degradation at the farm (upon harvest) and later during the milling and baking processes. However, less attention has been paid to changes in grain quality between harvest and milling. This paper aims to address this knowledge gap and discusses possible strategies for preserving grain quality (for Canadian wheat in particular) during unit operations at primary, process, or terminal elevators. To this end, the importance of wheat flour quality metrics is briefly described, followed by a discussion on the effect of grain properties on such quality parameters. This work also explores how drying, storage, blending, and cleaning, as some of the common post-harvest unit operations, could affect grain's end-product quality. Finally, an overview of the available techniques for grain quality monitoring is provided, followed by a discussion on existing gaps and potential solutions for quality traceability throughout the wheat supply chain.

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Examination of wheat kernels for the presence of Fusarium damage and mycotoxins using near-infrared hyperspectral imaging

Cited by 23 publications

References 36 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

Enhancing traceability of wheat quality through the supply chain

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