Authors : Sneha Jetani; Jensi Ghelani; Reena Desai

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/4b6mpnfe

Scribd : https://tinyurl.com/38v32vhj

DOI : https://doi.org/10.38124/ijisrt/26May290

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Crop diseases continue to pose a serious danger to agricultural productivity worldwide, resulting in large losses in crop quality, yield, and economic value. For large-scale farming, traditional disease detection techniques, which mostly rely on specialist knowledge and manual examination, are frequently laborious, subjective and ineffective. Deep learning (DL), a branch of artificial intelligence, has become a potent method for automated and precise crop disease prediction because to developments in information technology. With an emphasis on image-based analysis and data-driven modelling, this paper provides a thorough overview of current advancements in deep learning-based methods for crop disease diagnosis and prediction. Convolutional Neural Networks (CNNs), one type of deep learning architecture, have shown exceptional performance in reliably diagnosing different crop illnesses and extracting complicated characteristics from plant photos. The detection accuracy has been further enhanced by advanced versions like ResNet, VGGNet, and EfficientNet, which frequently surpass 95 percentage under controlled circumstances. Precision agriculture techniques have been improved by the real-time monitoring and early disease identification made possible by the integration of deep learning with Internet of Things (IoT) devices, remote sensing technologies, and drone-based imaging systems. Despite these developments, a number of problems still exist, such as the requirement for sizable labelled datasets, high processing demands, overfitting problems, and restricted model generalisation in practical settings. This paper identifies these drawbacks and explores possible remedies, such as explainable deep learning methods, data augmentation, and transfer learning. Future research will focus on integrating intelligent decision-support systems, scalable deployment approaches, and edge computing. All things considered, deep learning-based crop disease prediction systems have enormous potential to revolutionise contemporary agriculture by facilitating early intervention, enhancing crop health management, and encouraging sustainable farming methods.

Keywords : Deep Learning; Crop Disease Prediction; Convolutional Neural Networks (CNN); Precision Agriculture; Image-Based Disease Detection; Artificial Intelligence; Remote Sensing; IoT in Agriculture.

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