Coffee Disease Visualization and Classification

Yebasse, Milkisa; Shimelis, Birhanu; Warku, Henok Tegegn; Ko, Jeong Hoon; Cheoi, Kyung Joo

doi:10.3390/plants10061257

Cited by 28 publications

(15 citation statements)

References 37 publications

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“…GradCAM, a heatmap-based feature attribution method, was used to explain the model [ 22 ]. In contrast to CAM, GradCAM does not require modification of the network structure and has been validated in the literature on DL for assigning feature importance to different areas of images [ 17 , 23 ]. By extracting features in areas corresponding to human interpretation, this method rapidly confirmed whether the models constructed herein were behaving as expected.…”

Section: Methodsmentioning

confidence: 99%

Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning

et al. 2022

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Background: Deep learning (DL) methods can noninvasively predict glioma subtypes; however, there is no set paradigm for the selection of network structures and input data, including the image combination method, image processing strategy, type of numeric data, and others. Purpose: To compare different combinations of DL frameworks (ResNet, ConvNext, and vision transformer (VIT)), image preprocessing strategies, magnetic resonance imaging (MRI) sequences, and numerical data for increasing the accuracy of DL models for differentiating glioma subtypes prior to surgery. Methods: Our dataset consisted of 211 patients with newly diagnosed gliomas who underwent preoperative MRI with standard and diffusion-weighted imaging methods. Different data combinations were used as input for the three different DL classifiers. Results: The accuracy of the image preprocessing strategies, including skull stripping, segment addition, and individual treatment of slices, was 5%, 10%, and 12.5% higher, respectively, than that of the other strategies. The accuracy increased by 7.5% and 10% following the addition of ADC and numeric data, respectively. ResNet34 exhibited the best performance, which was 5% and 17.5% higher than that of ConvNext tiny and VIT-base, respectively. Data Conclusions: The findings demonstrated that the addition of quantitatively numeric data, ADC images, and effective image preprocessing strategies improved model accuracy for datasets of similar size. The performance of ResNet was superior for small or medium datasets.

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Section: Methodsmentioning

confidence: 99%

Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning

et al. 2022

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“…The AI and ML algorithms have been successfully applied in a wide range of agroenvironmental areas, including: plant-based [124,[129][130][131][132][133], pedological [134][135][136][137], and salinity-based [116,120,138] studies (Table 2). However, as soil salinization is commonly a highly complex and nonlinear variable [12], the data processed by AI and ML techniques could result in better outcomes vs. classical statistical methods in soil salinity classification and prediction.…”

Section: Exploration Of Salinization Processes By Artificial Intellig...mentioning

confidence: 99%

“…The AI and ML algorithms have been successfully applied in a wide range of agro-environmental areas, including: plant-based [ 124 , 129 , 130 , 131 , 132 , 133 ], pedological [ 134 , 135 , 136 , 137 ], and salinity-based [ 116 , 120 , 138 ] studies ( Table 2 ).…”

Section: Exploration Of Salinization Processes By Artificial Intellig...mentioning

confidence: 99%

Salt Stress in Plants and Mitigation Approaches

Ondrašek

Rathod

Manohara

et al. 2022

Plants

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Salinization of soils and freshwater resources by natural processes and/or human activities has become an increasing issue that affects environmental services and socioeconomic relations. In addition, salinization jeopardizes agroecosystems, inducing salt stress in most cultivated plants (nutrient deficiency, pH and oxidative stress, biomass reduction), and directly affects the quality and quantity of food production. Depending on the type of salt/stress (alkaline or pH-neutral), specific approaches and solutions should be applied to ameliorate the situation on-site. Various agro-hydrotechnical (soil and water conservation, reduced tillage, mulching, rainwater harvesting, irrigation and drainage, control of seawater intrusion), biological (agroforestry, multi-cropping, cultivation of salt-resistant species, bacterial inoculation, promotion of mycorrhiza, grafting with salt-resistant rootstocks), chemical (application of organic and mineral amendments, phytohormones), bio-ecological (breeding, desalination, application of nano-based products, seed biopriming), and/or institutional solutions (salinity monitoring, integrated national and regional strategies) are very effective against salinity/salt stress and numerous other constraints. Advances in computer science (artificial intelligence, machine learning) provide rapid predictions of salinization processes from the field to the global scale, under numerous scenarios, including climate change. Thus, these results represent a comprehensive outcome and tool for a multidisciplinary approach to protect and control salinization, minimizing damages caused by salt stress.

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“…The remaining 5% is produced by larger estates, and coffee plantations larger than 50 ha are rare outside Central and South America [5]. For instance, in Ethiopia, coffee is the top export, making between 20-25% of all foreign exchange earnings, and around 15 million people are thought to depend on coffee for their livelihood; more than 80% of coffee growers are peasant farmers [6].…”

Section: Introduction 1coffee As One Of the Most Appealing Beveragesmentioning

confidence: 99%

Coffee-Associated Endophytes: Plant Growth Promotion and Crop Protection

Asad¹,

Priyashantha²,

Tibpromma³

et al. 2023

Preprint

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Endophytic microbes are a ubiquitous group of plant-associated communities that colonize the intercellular or intracellular host tissues while giving numerous beneficial effects to the plants. All the plant species are thought to be associated with endophytes, majorly constituted with bacteria and fungi. During the last two decades, there has been a considerable movement toward the study of endophytes associated with coffee plants. In this review, the main consideration is given to address the coffee-associated endophytic bacteria and fungi, particularly their action on plant growth promotion and the biocontrol of pests. In addition, we sought to identify and analyze the gaps in available research. Additionally, the potential of endophytes to improve the quality of coffee seeds is briefly discussed. Even though there are limited studies on the subject, it has been well recognized the potentiality of coffee endophytes in plant growth promotion through enhancing nitrogen fixation, availability of minerals, nutrient absorption, secretion of phytohormones, and other bioactive metabolites. Further, the antagonistic effect against various coffee pathogenic bacteria, fungi, nematodes, and also insect pests lead to the protection of the crop. Furthermore, it is recognized that endophytes enhance the sensory characteristics of coffee as a new field of study.

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Coffee Disease Visualization and Classification

Cited by 28 publications

References 37 publications

Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning

Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning

Salt Stress in Plants and Mitigation Approaches

Coffee-Associated Endophytes: Plant Growth Promotion and Crop Protection

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