PAPER ID: IJIM/Vol. 9 (I) May 2024/9-15/2

AUTHOR: Er. Anju Saini^[I]Er. Khushboo Kamboj^[II]  Er.Ankush Aggarwal^[III]

TITLE: PERFORMANCE COMPARISON OF IMAGE SEGMENTATION MODELS in SAM

ABSTRACT: Food security for the 7 billion individuals on earth requires limiting yield harm by timely identification of crop diseases & illnesses. Most deep learning models for automated recognition of illnesses in plants experience the ill effects of the tragic defect that once tried on independent data, their output drops significantly [1]. Because of this, datasets accessible internet based like plant town comprise of 50000 pictures. These web-based pictures when segmented through different covering strategies, the number of lesions identified from two picture segmentation models showed a huge parameter distinction rate in their outcomes. This examination paper shows that disease forecast results rely upon the nature of the picture. Huge Model performed better compared to the Base Model.

KEYWORDS: Image Segmentation, Disease Prediction,  Lesion, Performance parameters

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