Smart Nutrient Management for Pest and Disease Control in Tropical Fruit Crops: A Literature Review for Practitioners

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Abstract

Smart nutrient management is gaining traction as a cornerstone of integrated pest and disease control, especially in tropical fruit crops like papaya and banana—key commodities in northern Australia. This review explores how nutrient imbalances, particularly excessive nitrogen, can worsen pest outbreaks such as mite infestations. Drawing on Mulder’s chart of nutrient interactions, we highlight how surplus nitrates can inhibit potassium uptake, weakening plant defences. We also examine emerging smart pest control strategies, including real-time nutrient diagnostics and intelligent monitoring systems, that offer practical tools for Australian growers. By blending scientific research with field-based insights, this paper aims to support sustainable farming practices and inform decision-making in Australia’s tropical horticulture sector. We also consider the role of AI technologies like deep learning and large language models in shaping the future of pest management.

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  1. This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17141765.

    This paper has the following pros and cons:

    Pros

    • Highlights the link between nutrient management and pest/disease susceptibility in tropical fruit crops.

    • Strong emphasis on sustainability and reducing chemical dependency.

    • Integrates advanced technologies (AI, sensors, deep learning) with agronomy practices.

    • Provides practical case studies (papaya, banana) relevant to tropical agriculture.

    • Promotes climate-smart pest management frameworks in line with global sustainability goals.

    • Acknowledges both biological control and agroecological approaches as complements to smart technologies.

    Cons

    • Heavy reliance on secondary literature rather than new experimental data.

    • Strong focus on Australian context, which may limit broader applicability.

    • Adoption challenges (cost, accessibility, farmer training) not deeply addressed.

    • Limited discussion of economic feasibility for smallholder farmers.

    • Technological optimism may overlook infrastructural and policy barriers.

    Competing interests

    The author declares that they have no competing interests.

    Use of Artificial Intelligence (AI)

    The author declares that they used generative AI to come up with new ideas for their review.