Template-Type: ReDIF-Article 1.0
Author-Name:Abdul  Rehman,  Muhammad  Akram,  Aashir  Waleed,  Arslan  Hafeez,  Abdul  Basit, Muhammad Zubair
Author-Email:aashir.walid@uet.edu.pk
Author-Workplace-Name: Department  of  Electrical  Electronics  and  Telecommunication  Engineering,  University  of Engineering and Technology, Faisalabad Campus, Faisalabad, Punjab, Pakistan 38000
Title:A Hybrid Model for Crop Disease Detection Based on Deep Learning and Support Vector Machine
Abstract:Pakistan's agriculture sector is the backbone of its economy, contributing significantly to its  gross  domestic  product  (GDP).  However,  a  key  challenge  in  this  sector  is  to counteract the crop diseases timely because these diseases result in reduced production, increased cost and eventually lead to economic loss. Traditional disease control methods are costly,   time-consuming,   and   often   lack   technical   support,   resulting   in   poor   disease management   and   harmful   environmental   consequences.   This   research   harnesses the unmatched  capability  of  Artificial  Intelligence  (AI)  and  deep  learning  for  timely  disease detection  in  crops. This  research  introduces  a  hybrid  model thatcombines  deep  learning models with a machine learning classifier for disease detection. AlexNet, Vgg-16, ResNet50, and  MobileNet are  the  deep  learning  models  that  have  been employed  for  the  detection  of various diseases in crop leaves of rice, potato,and corn. These models have been trained by using  healthy  and  diseased  leaf  images  of  the  mentioned  crops  and  then  these  models  are combined  with a Support  Vector  Machine  (SVM)  classifier  to  enhance  the  accuracy  of detection.  Experimental  results  show  the  outstanding  performance  of  this  hybrid  approach for timely disease detection in crops. It is further observed that the combination of MobileNet and SVM results in an impressive accuracy of 95.68% in disease detection. This technological approach  would  be  beneficial  for  farmers in  the effective  management  and control  of  crop diseases thus improving the crop yield and ultimately contributing to economic growth.
Keywords: Crops  Disease,  Artificial  Intelligence,  Support  Vector  Machines,  Deep Learning, Agriculture
Journal:International Journal of Innovations in Science and Technology
Pages:843-855
Volume:7
Issue:2
Year:2025
Month:May 
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1270/1896
File-Format: Application/pdf
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1270
File-Format: text/html
Handle: RePEc:abq:IJIST1:v:7:y:2025:i:2:p:843-855