ARTIFICIAL INTELLIGENCE DRIVEN DRONE OBSERVATION AND PEST CONTROL IN BANANA CROP: A SYSTEMATIC REVIEW

Abstract

Bananas are the most commonly eaten and significant fruit in global trade. Bananas are produced using a variety of methods and environments. In addition to regularly updating farmers on problems in banana plant leaves, this system aims to discover, diagnose, and treat banana leaf diseases. Customers' wants and lifestyles have changed significantly during the past few decades. These modifications provide additional difficulties for farmers whose output must satisfy consumer needs. Both the farmer and the consumer will benefit from the capacity to categorize agricultural products according to size and quality. In this case, the system gets its input in the form of standard photos of banana leaves taken using various image capture devices. It will then process those photos to identify any diseases and alert the farmer. Additionally, the system will advise the farmer on what to do next, including which fertilizers, herbicides, and agricultural practices to employ in order to prevent illnesses from harming neighboring crops. In this systematic review, useful and efficient methods for identification are presented in works that fall under the categories of image classification, AI/ML, deep learning, and mobile applications.

Plant phenotyping relevance

バナナ葉の画像から病害を識別する画像分類・AI/ML・深層学習手法を体系的にレビューしており、植物の病害状態を観測するフェノタイピング手法が中心です。

Code and data availability

This is a systematic review of banana disease detection methods. It references third-party datasets (e.g., PlantVillage) and reviewed studies, but contains no authors' own phenotype datasets, images, code, models, or supplements with availability statements. PlantVillage is cited prior work, not a paper-specific asset.

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