Survey Analysis on Disease Prediction for Different Plants
Keywords:
Sustainability in agriculture, High prediction, disease Prediction, Performance evaluation, machine learning, effective solution on yield.Abstract
Plant diseases can cause major crop losses if not identified early. Crop losses can thus be significant to plant diseases which are not recognized in time. Recent advances in Artificial Intelligence (AI), most notably, Machine Learning (ML) and Deep Learning (DL) have improved considerably the plant disease detection accuracy and speed. The survey paper reviews a number of research works based on various ML and DL models- Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Generative Adversarial Networks (GANs) and others. It visits their advantages, data files accessed, constraints and operability in real-time. A few of the studies find ways of integrating the mobile and web platforms to have practical deployments. The survey ends with the comments on the standard problems including data imbalances, environmental considerations and an ability to become adaptive and act in real-time in the context of intelligent farming systems.
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