Prediction of Citrus Diseases Using Machine Learning And Deep Learning: Classifier, Models SLR

TL;DR

This paper explores the application of machine learning and deep learning techniques to predict citrus diseases, aiming to improve early detection and management strategies to reduce economic losses.

Contribution

It introduces specific classifiers and models, such as SLR, for effective citrus disease prediction using machine learning and deep learning methods.

Findings

High accuracy in disease classification models

Effective early detection of citrus diseases

Potential for improved disease management strategies

Abstract

Citrus diseases have been major issues for citrus growing worldwide for many years they can lead significantly reduce fruit quality. the most harmful citrus diseases are citrus canker, citrus greening, citrus black spot, citrus leaf miner which can have significant economic losses of citrus industry in worldwide prevention and management strategies like chemical treatments. Citrus diseases existing in all over the world where citrus is growing its effects the citrus tree root, citrus tree leaf, citrus tree orange etc. Existing of citrus diseases is highly impact on economic factor that can also produce low quality fruits and increased the rate for diseases management. Sanitation and routine monitoring can be effective in managing certain citrus diseases, but others may require more intensive treatments like chemical or biological control methods.