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Crops hold paramount significance as they serve as the primary provider ofenergy, nutrition, and medicinal benefits for the human population. Plantdiseases, however, can negatively affect leaves during agriculturalcultivation, resulting in significant losses in crop output and economic value.Therefore, it is crucial for farmers to identify crop diseases. However, thismethod frequently necessitates hard work, a lot of planning, and in-depthfamiliarity with plant pathogens. Given these numerous obstacles, it isessential to provide solutions that can easily interface with mobile and IoTdevices so that our farmers can guarantee the best possible crop development.Various machine learning (ML) as well as deep learning (DL) algorithms havebeen created & studied for the identification of plant disease detection,yielding substantial and promising results. This article presents a novelclassification method that builds on prior work by utilising attention-basedfeature extraction, RGB channel-based chromatic analysis, Support VectorMachines (SVM) for improved performance, and the ability to integrate withmobile applications and IoT devices after quantization of information. Severaldisease classification algorithms were compared with the suggested model, andit was discovered that, in terms of accuracy, Vision Transformer-based featureextraction and additional Green Chromatic Coordinate feature with SVMclassification achieved an accuracy of (GCCViT-SVM) - 99.69%, whereas afterquantization for IoT device integration achieved an accuracy of - 97.41% whilealmost reducing 4x in size. Our findings have profound implications becausethey have the potential to transform how farmers identify crop illnesses withprecise and fast information, thereby preserving agricultural output andensuring food security.

Crop Disease Classification using Support Vector Machines with Green  Chromatic Coordinate (GCC) and Attention based feature extraction for IoT  based Smart Agricultural Applications