A Review On Machine Learning And Deep Learning Models For Pest Detection For Precision Agriculture Applications

• Author(s):

  Rajendra Mandloi | Prof. Pradeep Pal

• Keywords:

  Precision Agriculture, White Fly Pest Detection, Image Processing, Segmentation, Feature Extraction, Machine Learning, Accuracy.

• Abstract:

  Agriculture Constantly Faces Various Challenges Including Attacks From New Pests And Insects. Often, With Large Farm Sizes And Plummeting Manpower In The Agricultural Sector, It Becomes Challenging To Continuously Monitor Crops For Pest Infestation. Precision Agriculture Has Emerged As A Promising And Much Sought After Technique For Automated And Quick Detection Of Pests In Agricultural Farms. With The Advent Of Inexpensive And Compact Drones, Image Capturing And Processing Techniques Based On Machine Learning, Automated Detection Of Pest Attacks Has Gained Prominence. In This Research Paper, A Specific Type Of Pest Attack Known As The White Fly Attack Has Been Investigated Which Affects A Variety Of Crops. This Paper Presents A Detailed Background Of Precision Agriculture Based Techniques For Automated Detection Of White Fly Attacks On Crops. A Thorough Investigation Of Image Enhancement, Segmentation, Feature Extraction And Classification Pertaining To White Fly Attacks Has Been Resented. Salient Features Of The Contemporary Techniques Used For The Purpose Have Been Cited And Evaluated.

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Other Details

• Paper id:

  IJSARTV12I6105749

• Published in:

  Volume: 12 Issue: 6 June 2026

• Publication Date:

  2026-06-29

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