Tillage Practices in Potato (Solanum tuberosum L.) Production: A Review

Djaman, Koffi; Koudahe, Komlan; Koubodana, Houteta D.; Saibou, Aminou; Essah, S. Y. C.

doi:10.1007/s12230-021-09860-1

Cited by 24 publications

(17 citation statements)

References 88 publications

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“…This can be explained by the fact that potato is typically planted in rows, between which a sizable soil area is left bare and exposed to detachment by rainsplash and overland flow. Growing potato also involves activities that disturb the soil, such as frequent tillage for fine seedbed preparation during the dry season and weeding and hilling (digging and piling) during the main rainy season (Nyawade et al, 2018;Djaman et al, 2022). Case studies in other regions noted that soil erodibility is typically much higher in potato croplands than in other croplands (Lilly et al, 2009;Tiessen et al, 2009;Yu et al, 2014).…”

Section: Runoff and Soil Loss Amounts In Relation To Land Use Typementioning

confidence: 99%

Land use, management and climate effects on runoff and soil loss responses in the highlands of Ethiopia

Ebabu

Taye

Tsunekawa

et al. 2023

Journal of Environmental Management

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Section: Runoff and Soil Loss Amounts In Relation To Land Use Typementioning

confidence: 99%

Land use, management and climate effects on runoff and soil loss responses in the highlands of Ethiopia

Ebabu

Taye

Tsunekawa

et al. 2023

Journal of Environmental Management

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“…Potato (Solanum tuberosum L.) is one of the main food products and a source of vitamins and minerals [1]. According to the statistics of the World Potato Congress organization for 2020-2021, Russia is included in the list of 25 main world leaders in terms of potato production [2].…”

Section: Introductionmentioning

confidence: 99%

Levels of physiological activity of antioxidant enzymes in potatoes (S. tuberosum L.) when infected PVS virus

Киргизова

Калашникова²,

Turpanova³

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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This article presents the results of changes in the activity of potato antioxidant enzymes during infection with the PVS virus. As a result of the research, it was found that the formation of callus to obtain microclones is more efficient when using leaf explants, compared with stem explants in all studied varieties. The article presents that varieties with different susceptibility have different enzymatic activity, which can serve as a marker trait when ranking potatoes in breeding. Interestingly, in potato microclones, the activity of POX, CAT and SOD upon infection was significantly higher compared to the control. At the same time, the greatest differences in the physiological response were noted in the activity of POX in a microclone obtained from a potato variety resistant to viruses, and, on the contrary, a decrease in activity was noted in a microclone from a susceptible variety. However, microclones of the susceptible variety showed higher CAT activity than other genotypes. The highest SOD activity was noted in microclones of resistant potato varieties compared to other genotypes. Our data show that the virus can cause changes in the spectrum of antioxidant enzymes, which was characterized by the appearance of additional isoforms in microclones of Khozyayushka and Alena cultivars, which indicated the formation of more stable protection in these accessions.

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“…Potato is the fourth largest food crop globally after wheat, rice, and corn. At present, with potato cultivation area and total production increasing year by year [ 1 ], potato disease problems are receiving increasing attention. Among the many leaf diseases of potatoes, early and late blight are common diseases caused by a fungal infection.…”

Section: Introductionmentioning

confidence: 99%

DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification

Dai

Fan

2022

Computational Intelligence and Neuroscience

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To solve the problems of weak generalization of potato early and late blight recognition models in real complex scenarios, susceptibility to interference from crop varieties, colour characteristics, leaf spot shapes, disease cycles and environmental factors, and strong dependence on storage and computational resources, an improved YOLO v5 model (DA-ActNN-YOLOV5) is proposed to study potato diseases of different cycles in multiple regional scenarios. Thirteen data augmentation techniques were used to expand the data to improve model generalization and prevent overfitting; potato leaves were extracted by YOLO v5 image segmentation and labelled with LabelMe for building data samples; the component modules of the YOLO v5 network were replaced using model compression technology (ActNN) for potato disease detection when the device is low on memory. Based on this, the features extracted from all network layers are visualized, and the extraction of features from each network layer can be distinguished, from which an understanding of the feature learning behavior of the deep model can be obtained. The results show that in the scenario of multiple complex factors interacting, the identification accuracy of early and late potato blight in this study reached 99.81%. The introduced data augmentation technique improved the average accuracy by 9.22%. Compared with the uncompressed YOLO v5 model, the integrated ActNN runs more efficiently, the accuracy loss due to compressed parameters is less than 0.65%, and the time consumption does not exceed 30 min, which saves a lot of computational cost and time. In summary, this research method can accurately identify potato early and late blight in various scenarios.

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Tillage Practices in Potato (Solanum tuberosum L.) Production: A Review

Cited by 24 publications

References 88 publications

Land use, management and climate effects on runoff and soil loss responses in the highlands of Ethiopia

Land use, management and climate effects on runoff and soil loss responses in the highlands of Ethiopia

Levels of physiological activity of antioxidant enzymes in potatoes (S. tuberosum L.) when infected PVS virus

DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification

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