AI for Crop Disease Detection

Abstract

Turmeric and ginger are economically vital spice crops cultivated for their underground rhizomes, which are largely susceptible to conditions similar as soft spoilage, rhizome spoilage, and bacterial wilt. Traditional discovery styles calculate on visual examination orpost- harvest opinion, frequently performing in delayed treatment and significant yield loss. This exploration proposes an AI- driven frame for early rhizome complaint discovery using a multimodal approach that integrates deep literacy, hyperspectral imaging, and IoT- grounded environmental seeing. Convolutional Neural Networks (CNNs), enhanced through transfer literacy, are employed to classify rhizome health from subterranean image data, while detector emulsion ways relate soil humidity, temperature, and pH with complaint onset. The system also incorporates time- series soothsaying and natural language interfaces to deliver real- time cautions and treatment recommendations to growers. By fastening on rhizome- position analysis — an area largely overlooked in being literature — this study aims to ameliorate individual delicacy, reduce crop losses, and promote sustainable spice husbandry through intelligent, accessible technology.

Code and data availability

The paper describes a custom rhizome image dataset, sensor data, and models, but provides no public deposit, availability statement, or URL for any dataset, code, or trained model. No paper-specific public asset is actionable.

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