Mapping and predicting cassava mosaic disease outbreaks using earth observation and meteorological data-driven approaches

Chaiyana, Akkarapon; Khiripet, Noppadon; Ninsawat, Sarawut; Siriwan, Wanwisa; Shanmugam, Mohana Sundaram; Virdis, Salvatore G.P.

doi:10.1016/j.rsase.2024.101231

Cited by 2 publications

(1 citation statement)

References 117 publications

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“…Cassava mosaic disease (CMD) is a significant threat to cassava production in various regions, particularly in Africa (Thuy et al, 2021;Malik et al, 2022;Sheat et al, 2022;Shirima et al, 2022;Chaiyana et al, 2024). The disease is caused by several geminiviruses, including African cassava mosaic virus (ACMV) (Naseem & Winter, 2016;Alabi & Mulenga, 2017), East African cassava mosaic virus (EACMV) (Naseem & Winter, 2016), and East African cassava mosaic Cameroon virus (EACMCMV) (Kouakou et al, 2024).…”

Section: Introductionmentioning

confidence: 99%

A random forest-based analysis of cassava mosaic disease-related factors affecting the on-farm livelihoods of cassava farmers

Claudette,

Roger

2024

jaar

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This study aimed to identify key CMD-related factors affecting Cameroon cassava farmers’ incomes originating from both the sale of cassava cuttings (V215) and the sale of cassava roots (V216). To achieve this, nine CMD-related variables were used to independently train two Random Forest models. These models were later employed for regression-based prediction of both financial targets V215 and V216. The Random Forest (RF)-based mean absolute percentage error for targets V215 and V216 were 0.19 and 1.25 respectively. The RF-based mean Gaussian deviance for targets V215 and V216 were 0.07 and 0.51 respectively. Based on RF feature importance scores (RFFI), the top 3 factors affecting income originating from the sale of cassava cuttings were found to be: late appearance of symptoms as a difficulty associated with regular field monitoring (RFFI of 0.2594), removal of infected plants as a method of controlling frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1633) and lack of healthy planting material due to frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1495). Also, the top 3 factors affecting income originating from the sale of cassava roots were found to be: the replacement of infected plants with healthy cuttings as a method of controlling the frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1974), decrease in yield due to frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1530) and poor plant growth due to frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1388).

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Section: Introductionmentioning

confidence: 99%

A random forest-based analysis of cassava mosaic disease-related factors affecting the on-farm livelihoods of cassava farmers

Claudette,

Roger

2024

jaar

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Sugarcane sustainability in a changing climate: a systematic review on pests, diseases, and adaptive strategies

Msomba,

Ndaki,

Joseph

2024

Front. Agron.

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The cultivation of sugarcane (Saccharum officinarum L.) in the face of climate change requires robust strategies for managing pests, diseases, and weeds. This systematic review exposes critical deficiencies in current practices and underscores the need for climate-adaptive strategies. Climate change differentially influences pest behaviour, disease progression, and weed growth across various regions, yet the lack of region-specific responses impairs effective management. The review emphasizes the necessity for localized approaches that consider specific climatic conditions and the development of predictive models to anticipate pest and disease outbreaks. These models include Decision Support Systems (DSS), Support Vector Machines (SVM), Susceptible-Exposed-Infectious-Recovered (SEIR) models, Geographic Information Systems (GIS), Species Distribution Models (SDMs), Agricultural Production Systems sIMulator (APSIM), and Integrated Pest Management (IPM). Crucial strategies encompass integrated pest and disease management, adaptive breeding, precision agriculture, and ongoing innovation. Precision agriculture technologies, such as remote sensing and drones, enable early detection and prompt interventions. By adopting these adaptive measures and addressing existing research gaps, the sugarcane industry can bolster its resilience and maintain productivity amidst evolving climatic conditions.Systematic review registrationhttps://www.bmj.com/content/372/bmj.n71.

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Mapping and predicting cassava mosaic disease outbreaks using earth observation and meteorological data-driven approaches

Cited by 2 publications

References 117 publications

A random forest-based analysis of cassava mosaic disease-related factors affecting the on-farm livelihoods of cassava farmers

A random forest-based analysis of cassava mosaic disease-related factors affecting the on-farm livelihoods of cassava farmers

Sugarcane sustainability in a changing climate: a systematic review on pests, diseases, and adaptive strategies

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