Fusion of Robotics, AI, and Thermal Imaging Technologies for Intelligent Precision Agriculture Systems.

Abstract

The world population is expected to grow to over 10 billion by 2050 and therefore impose further stress on food production. Precision agriculture has become the main approach used to enhance productivity with sustainability in agricultural production. This paper conducts a technical review of how robotics, artificial intelligence (AI), and thermal imaging (TI) technologies transform precision agriculture operations, focusing on sensing, automation, and farm decision making. Agricultural robots promote labor solutions and efficiency by utilizing their sensing devices and kinematics in planting, spraying, and harvesting. Through accurate assessment of pests/diseases and quality assurance of the harvested crops, AI and TI bring efficiency to the crop monitoring sector. Different deep learning models are employed for plant disease diagnosis and resource management, namely the VGG16 model, InceptionV3, and MobileNet; the PlantVillage, PlantDoc, and FieldPlant datasets are used respectively. To reduce crop losses, AI-TI integration enables early recognition of fluctuations caused by pests or diseases, allowing control and mitigation in good time. While the issues of cost and environmental variability (illumination, canopy moisture, and microclimate instability) are taken into consideration, the advancement in artificial intelligence, robotics technology, and combined technologies will offer sustainable solutions to the existing gaps.

Plant phenotyping relevance

ロボティクス、AI、熱画像による植物センシングと病害診断を技術レビューとして扱っており、植物の病害状態を取得・推定する方法が主要な内容に含まれるため。

Code and data availability

This is a review article with no original phenotyping measurements, datasets, images, models, or analysis code of its own. The plant disease datasets mentioned (PlantVillage, PlantDoc, FieldPlant) are cited prior-work resources, not paper-specific assets, and no author code or data availability statement appears in the

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