mPD-APP: a mobile-enabled plant diseases diagnosis application using convolutional neural network toward the attainment of a food secure world
Emmanuel Oluwatobi Asani,
Yomi Phineas Osadeyi,
Adekanmi A. Adegun
et al.
Abstract:The devastating effect of plant disease infestation on crop production poses a significant threat to the attainment of the United Nations' Sustainable Development Goal 2 (SDG2) of food security, especially in Sub-Saharan Africa. This has been further exacerbated by the lack of effective and accessible plant disease detection technologies. Farmers' inability to quickly and accurately diagnose plant diseases leads to crop destruction and reduced productivity. The diverse range of existing plant diseases further … Show more
“…Pearson correlation coefficient between the digital IMF content and the IMF content measured by NIRS reached 0.74, proving the feasibility of using Pork IMF App to measure IMF content. Recently, a few methodologies based on mobile App have been successfully used to perform high‐throughput phenotyping in plants (Asani et al., 2023; Müller‐Linow et al., 2019; Röckel et al., 2022). Our research presents a case study of using a mobile device for animal phenotyping.…”
Section: Discussionmentioning
confidence: 99%
“…Mitoregulin encoded by MTLN is a conserved mitochondrial peptide that has been demonstrated to control fatty acid oxidation and regulate lipolysis in adipocytes. Accumulation of triglycerides and fat was observed in MTLN knockout mice (Averina et al, 2023;Friesen et al, 2020). NDUFAB1, a subunit of NADH dehydrogenase, is necessary for maintaining systemic glucose homeostasis.…”
Intramuscular fat (IMF) content and distribution significantly contribute to the eating quality of pork. However, the current methods used for measuring these traits are complex, time‐consuming and costly. To simplify the measurement process, this study developed a smartphone application (App) called Pork IMF. This App serves as a rapid and portable phenotyping tool for acquiring pork images and extracting the image‐based IMF traits through embedded deep‐learning algorithms. Utilizing this App, we collected the IMF traits of the longissimus dorsi muscle in a crossbred population of Large White × Tongcheng pigs. Genome‐wide association studies detected 13 and 16 SNPs that were significantly associated with IMF content and distribution, respectively, highlighting NR2F2, MCTP2, MTLN, ST3GAL5, NDUFAB1 and PID1 as candidate genes. Our research introduces a user‐friendly digital phenotyping technology for quantifying IMF traits and suggests candidate genes and SNPs for genetic improvement of IMF traits in pigs.
“…Pearson correlation coefficient between the digital IMF content and the IMF content measured by NIRS reached 0.74, proving the feasibility of using Pork IMF App to measure IMF content. Recently, a few methodologies based on mobile App have been successfully used to perform high‐throughput phenotyping in plants (Asani et al., 2023; Müller‐Linow et al., 2019; Röckel et al., 2022). Our research presents a case study of using a mobile device for animal phenotyping.…”
Section: Discussionmentioning
confidence: 99%
“…Mitoregulin encoded by MTLN is a conserved mitochondrial peptide that has been demonstrated to control fatty acid oxidation and regulate lipolysis in adipocytes. Accumulation of triglycerides and fat was observed in MTLN knockout mice (Averina et al, 2023;Friesen et al, 2020). NDUFAB1, a subunit of NADH dehydrogenase, is necessary for maintaining systemic glucose homeostasis.…”
Intramuscular fat (IMF) content and distribution significantly contribute to the eating quality of pork. However, the current methods used for measuring these traits are complex, time‐consuming and costly. To simplify the measurement process, this study developed a smartphone application (App) called Pork IMF. This App serves as a rapid and portable phenotyping tool for acquiring pork images and extracting the image‐based IMF traits through embedded deep‐learning algorithms. Utilizing this App, we collected the IMF traits of the longissimus dorsi muscle in a crossbred population of Large White × Tongcheng pigs. Genome‐wide association studies detected 13 and 16 SNPs that were significantly associated with IMF content and distribution, respectively, highlighting NR2F2, MCTP2, MTLN, ST3GAL5, NDUFAB1 and PID1 as candidate genes. Our research introduces a user‐friendly digital phenotyping technology for quantifying IMF traits and suggests candidate genes and SNPs for genetic improvement of IMF traits in pigs.
“…Over the years plant disease detection in sub-Saharan Africa has been classically and traditionally carried out by on the spot assessment of visible plant parts due in large part to limited or non-existent access to user-friendly advanced technologies (Asani et al, 2023). Internet of things refers to collective network of connected devices and technology that facilitates communication between devices and the cloud as well as between devices themselves (Amazon, 2023).…”
Section: Internet Of Things (Iot) and Cell Phone Image-based Plant Di...mentioning
Agriculture remains a mainstay gross domestic product earner and key contributor for public health nutrition of various economies in sub-Saharan Africa. Pests and disease attacks, and ecological health effects are some of the vital challenges to agro-productivity and sustainability. Early detection, proper diagnosis, accurate and precise measurement or correct estimation of disease would help mitigate the losses encountered by the farm economy from these bio-pressures which already are known to exceed 50% postharvest. Classical and conventional approaches to crop loss assessments though useful but whose accuracy depends largely on the skill of the assessors is on the other hand time consuming and prone to some forms of biases. Modern technology through remote sensing techniques and various other forms of satellite-based or airplane-borne sensing systems including radar, GIS, GPS and drones hold strong potentials for improving disease detection, disease or damage assessment and crop loss estimation. Herein, a review highlighting some of the standard and innovative techniques of plant disease, biotic and abiotic stress detection, assessment, measurement and crop loss estimation is presented.
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