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Background: South Africa is one of the largest importers of pesticides in Africa. Pesticides play an integral role in food security and ensuring economic survival. Cypermethrin is a type II pyrethroid and is commonly and widely used as an insecticide. Poisoning arises from exposure through inhalation, skin contact or ingestion. Its toxic effects manifest predominantly as neurological and gastrointestinal symptoms. Severe pyrethroid poisonings are rare but can present as an organophosphate-like toxidrome. This poses a diagnostic conundrum. Case report: This case concerns a 36-year-old male from a rural town in the Eastern Cape province of South Africa, who was diagnosed with intentional cypermethrin poisoning after a suicide attempt. He was initially diagnosed as a suspected organophosphate poisoning with coma. He made a full recovery after mechanical ventilation and symptomatic treatment with a low dose muscarinic antagonist, atropine sulphate. Conclusions: With the ease of over-the-counter procurement, cypermethrin and other pyrethroid poisonings pose an increasing diagnostic dilemma to frontline and critical care personnel. This case report intends to raise awareness about the organophosphate-like toxidrome at presentation and the potential complications of high dose atropine treatment, commonly used to treat organophosphate poisoning. The treatment of cypermethrin is largely supportive with dermal decontamination of skin as an essential component. Low dose atropine may be utilized if required.

Modeling the intricate interactions between fruit trees, their environments, soils, and economic factors continues to be a significant challenge in agricultural research globally, requiring a multidisciplinary approach. Despite advances in agricultural technology and algorithms, significant knowledge gaps persist in understanding and modeling these interactions. This review explores basic concepts related to modeling for tropical fruit production. It explains modeling development from sensor technologies, image analysis, databases, and algorithms for decision support systems while considering climate changes or edaphoclimatic limitations. We report the current fruit modeling tendencies showing a significant increase in publications on these topics starting in 2021, driven by the need for sustainable solutions and access to large agricultural databases. This study emphasizes inherent challenges in tropical fruit modeling, such as fruit tree cycles, costly and time-consuming experimentation, and the lack of standardized data. These limitations are evident in tropical fruit, where few models have been reported or validated for cocoa, avocado, durian, dragonfruit, banana, mango, or passion fruit. This study analyzes the classification of the algorithms related to tropical fruit into three main categories: supervised, unsupervised, and reinforcement learning, each with specific applications in agricultural management optimization. Crop classification and yield prediction use supervised models like neural networks and decision trees. Unsupervised models, like K-Means clustering, allow pattern identification without prior labels, which is useful for area segmentation and pest detection. Automation of irrigation and fertilization systems employs reinforcement learning algorithms to maximize efficiency. This multidisciplinary review discusses recent approaches to 1) Modeling Soil health and plant-soil interaction, 2) Yield prediction in tropical fruit orchards, 3) Integrating meteorological models for enhanced tropical fruit production, and 4) Economics of tropical fruit business through modeling. Furthermore, this review illustrates the complexity and multidisciplinary research on models for tropical fruit and platforms using agricultural models. Further opportunities to advance fruit modeling frameworks are indicated, requiring technical knowledge about the fruit crop requirements with user-friendly platforms to collect and access fruit tree data and site-specific agroecological conditions. [ABSTRACT FROM AUTHOR]