Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images

Yi, Jinhui; Krusenbaum, Lukas; Unger, Paula; Hüging, Hubert; Seidel, Sabine J.; Schaaf, Gabriel; Gall, Jüergen

doi:10.3390/s20205893

Cited by 33 publications

(21 citation statements)

References 35 publications

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“…Zhou et al [33] presented the pattern recognition algorithm including L*, a*, Entropy × Density fusion features and SVM, and orientation code matching (OCM)-based template matching algorithm to observe the development of CLS disease in sugar beets under real field conditions. In addition, different studies were carried out to identify the diseases of sugar beet [34], to diagnose nutrient deficiencies [35], and to evaluate the effect of inoculation [36].…”

Section: Resultsmentioning

confidence: 99%

Effect of lacto-fermentation and freeze-drying on the quality of beetroot evaluated using machine vision and sensory analysis

Ropelewska

Wrzodak

Sabancı

et al. 2021

Eur Food Res Technol

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This study was aimed at evaluating the effect of freeze-drying and lacto-fermentation on the texture parameters of images and sensory attributes of beetroots. The samples were imaged using a flatbed scanner, and textures from images converted to color channels L, a, b, R, G, B, X, Y, Z were computed. The discrimination of raw and processed beetroots was performed using models based on textures selected for each color channel. The sensory quality of processed samples was determined using the attributes related to smell, color, texture and taste. The highest discrimination accuracy of 97.25% was obtained for the model built for color channel b. The accuracies for other channels were equal to 96.25% for channel a, 95.25% for channel R, 95% for channel Y, 94.75% for channel B, 94.5% for channel X, 94% for channel L, 92.5% for channel G, 88.25% for channel Z. In the case of some models, the raw and lacto-fermented beetroots were discriminated with 100% correctness. The freeze-dried and freeze-dried lacto-fermented samples were also the most similar in terms of sensory attributes, such as off-odor, attractiveness color, beetroot color, crunchiness, hardness, bitter taste, overall quality. The results indicated that the image parameters and sensory attributes may be related.

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Section: Resultsmentioning

confidence: 99%

Effect of lacto-fermentation and freeze-drying on the quality of beetroot evaluated using machine vision and sensory analysis

Ropelewska

Wrzodak

Sabancı

et al. 2021

Eur Food Res Technol

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“…This this goes in line with Hoffmann [16] who stated that sugar yield is more determined by dry matter partitioning (sink) than by canopy formation (source). Also, the low but considerable topsoil mineral N values of 2 to 10 kg ha −1 [44] let us assume that N sources such as mineralization and atmospheric N deposition may have contributed to a low but sufficient N supply at that site.…”

Section: Full Fertilizationmentioning

confidence: 99%

“…The following are available online at https://www.mdpi.com/2077-047 2/11/1/21/s1, Table S1: Soil analysis data. Topsoil mineral N, plant available soil P and K (PCAL and KCAL) extracted with a calcium-acetate-lactate extract in kg ha-1 and topsoil pH value for the seven treatments and four sampling dates in 2019 at the long-term fertilizer experiment Dikopshof (taken from Yi et al [44]), Table S2: Number of analyzed replicates per sampling date and treatment for LAI, DM root, DM shoot, Root morphology (total root length, root diameter) and link basic connectivity. For link basic connectivity, the replicates correspond to segments of one sample per treatment and per sampling date; Table S3.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Sugar Beet Shoot and Root Phenotypic Plasticity to Nitrogen, Phosphorus, Potassium and Lime Omission

et al. 2020

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In low input agriculture, a thorough understanding of the plant-nutrient interactions plays a central role. This study aims to investigate the effects of nitrogen (N), phosphorus (P), and potassium (K) and liming omission on shoot growth as well as on topsoil root biomass, growth and morphology (tuber and fibrous roots) of sugar beet grown under field conditions at the Dikopshof long-term fertilizer experiment (Germany). Classical shoot observation methods were combined with root morphology and link measurements using an image analysis program. Omission of the nutrients N, P and K as well as of liming led to a significant decrease in shoot growth. Tuber yield was lowest for the unfertilized and the K omission treatment. The root shoot ratio was highest in the N deficient treatment. In the K omission treatment, a strategic change from a less herringbone root type (early stage) to a more herringbone root type (late stage), which is more efficient for the acquisition of mobile nutrients, was observed. By contrast, a change from a more herringbone (early stage) to a less herringbone root type (late stage) which is less expensive to produce and maintain was observed in the unfertilized treatment. We conclude that sugar beet alters its root morphology as a nutrient acquisition strategy.

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“…This method improves the accuracy of tomato gray spot recognition by introducing GIOU regression loss function, and uses a pre-training method that combines hybrid training and migration learning to improve the generalization ability of the model. Literature [13] compared the performance of five networks, namely, AlexNet [14], VGG-16 [15], ResNet-101, DenseNet-161 [16], and SqueezeNet [17] for nutrient deficiency symptom identification based on the Deep Nutrient Deficiency for Sugar Beet dataset and discussed their limitations.…”

Section: Introductionmentioning

confidence: 99%

DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection

Wang

Yang

et al. 2021

Information

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In response to the difficulty of plant leaf disease detection and classification, this study proposes a novel plant leaf disease detection method called deep block attention SSD (DBA_SSD) for disease identification and disease degree classification of plant leaves. We propose three plant leaf detection methods, namely, squeeze-and-excitation SSD (Se_SSD), deep block SSD (DB_SSD), and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected plant leaves disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. We chose the PlantVillage dataset after careful consideration because it contains images related to the domain of interest. This dataset consists of images of 14 plants, including images of apples, tomatoes, strawberries, peppers, and potatoes, as well as the leaves of other plants. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.

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Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images

Cited by 33 publications

References 35 publications

Effect of lacto-fermentation and freeze-drying on the quality of beetroot evaluated using machine vision and sensory analysis

Effect of lacto-fermentation and freeze-drying on the quality of beetroot evaluated using machine vision and sensory analysis

Sugar Beet Shoot and Root Phenotypic Plasticity to Nitrogen, Phosphorus, Potassium and Lime Omission

DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection

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