ARTIFICIAL INTELLIGENCE FOR PLANT DISEASE DETECTION, MONITORING, AND FORECASTING: ADVANCES, CHALLENGES, AND FUTURE GAPS

Abstract

Plant diseases are one of the main limiting factors in global agricultural productivity, causing significant losses and compromising food security. The increasing complexity of production systems and the limitations of traditional diagnostic methods, based mainly on visual assessment and laboratory analyses, have driven the incorporation of artificial intelligence (AI) in plant pathology. In this context, the present study aimed to synthesize the advances, challenges, and gaps related to the application of AI in the detection, monitoring, and forecasting of plant diseases. This is an integrative literature review, conducted through systematic searches in national and international scientific databases, encompassing studies that addressed machine learning techniques, deep learning, and hybrid models applied to plant pathology. Approaches based on RGB images, multispectral and hyperspectral data, integration with unmanned aerial vehicles (UAVs), and forecasting models based on climatic variables were analyzed. The results show that convolutional neural networks and temporal architectures, such as LSTM, have substantially increased the diagnostic accuracy and forecasting potential of the systems, especially when integrated with environmental data. However, challenges persist related to the generalization of the models, scarcity of representative databases, field variability, and high computational cost. It is concluded that AI represents a strategic tool for the transition from a from a reactive phytopathology to a predictive and decision-support approach. However, its consolidation under real cultivation conditions depends on robust agronomic validation, methodological standardization, and multidisciplinary integration, aiming at more precise, sustainable systems applicable to precision agriculture.

Plant phenotyping relevance

植物病害の画像・マルチスペクトル・ハイパースペクトル観測とAIによる検出・予測を主題とするレビューであり、植物状態（病害）の取得・推定手法が中心です。

Code and data availability

This is an integrative literature review on AI in plant disease detection with no paper-specific datasets, images, code, models, or supplements; no availability statements or URLs are present in the supplied blocks.

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