AI-Powered Plant Disease Prediction through Data Analytics and Smart Decision Systems

Abstract

Plant diseases remain one of the most significant threats to crop yields, farmer livelihoods and economic stability worldwide. Conventional methods of predicting these diseases, especially through manual inspections and laboratory tests, are slow, costly, and prone to inaccuracies, particularly in rural areas with limited resources. Recent advances in artificial intelligence (AI), computer vision, and agricultural data analytics have created a new opportunity to monitor plant health in real-time. With the incorporation of deep learning algorithms and precision agriculture datasets, it is now possible to predict plant diseases before they occur by analyzing leaf images, weather data, and soil health characteristics. In this paper, we proposed an integrated framework that bridges agricultural data analytics with machine learning (ML) models for a decision support system to provide timely interventions, minimize pesticide usage and reduce crop loss. A model that is flexible to weather, cost-effective for smallholder farmers, and provided in platforms that are easy to use as a technology. The framework includes cloud-based dashboards, predictive alerts and market-driven analytics to ensure that disease management strategies are both environmentally sustainable and economically viable. The evaluation of the results suggests that there is significant improvement in diagnosis times, detection accuracy and increased overall efficiency of decision-making. It also sheds some light on various socio-economic benefits flowing from the integration of AI in plant pathology and secures it as a transformative tool against sustainable, climate-resilient agriculture.

Plant phenotyping relevance

葉画像から植物病害状態を推定するAI手法と、その診断精度・検出時間を評価する枠組みが中心であり、植物の病害表現型を対象とするため採用。

Code and data availability

The paper describes a plant disease prediction framework using public image repositories (PlantVillage, PlantDoc, PlantCLEF) and field-survey images, but provides no author-specific public dataset, code, model checkpoint, or supplement with availability language or URL. The mentioned repositories are generic third- or

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