Tomato Disease Detection: A Comparative Study Of Transfer Learning With Inceptionv3, Densenet, And Vgg19

• Author(s):

  Jibrin Abdullahi Dallatu | Hassan Shaibu | Abdulrauf Jibrin Gulani | Yunusa Muhammed | Abdullahi Mai-Abba | Habibu Ibrahim Usman | Muhammad Muhammad Koyami | Alkali Nanami Lantewa

• Keywords:

• Abstract:

  Tomatoes Are A Major Vegetable Crop Across The Globe, Contributing Significantly To Agriculture And Human Health. Unfortunately, Tomato Cultivation Is Negatively Impacted By Diseases Which Lead To Significant Yield Losses That Hurt The Farmers' Livelihoods. In This Paper, We Present A Tomato Disease Detection System Which Utilizes Transfer Learning With Three Very Popular Deep Learning Models, InceptionV3, DenseNet, And VGG19. This Paper Presents A Comprehensive Comparative Study Of The Architectures Of These Models, Measuring And Investigating Their Accuracy And Efficiency For Diagnosing Common Tomato Diseases Before Ultimately Being Able To Determine The Most Capable Model For Agricultural Research In To The Future. Through The Evaluation Of The Performance Of These Models, We Provide Important Knowledge Of Their Strengths And Weaknesses, Which Can Underpin Evidence-based Disease Control Interventions. Our Results Are Indicative Of The Potential To Improve Crop Health Surveillance And Enhance Sustainable Farming Practices. This Work Not Only Advances The Use Of Computer Vision In Agriculture, But Also Provides Practical Interpretation For Farmers And Industry Actors. Developing Reliable Disease Identification Tools Can Enable Early Detection And Treatment Of Plant Disease That Would Result In Yield Losses And Boosting Farm Productivity. Moreover, The Inclusion Of Deep Learning Approaches In Agriculture Allows For Data-driven Decision That Provides And Optimizes Use Of Resources And Efficiencies In Operations. Collaborative Research That Emphasizes The Integration Of Artificial Intelligence To Modernize Agriculture Will Improve Collaboration Between Technology And Agriculture. These Findings Have Implications For Increasing Food Security, Revitalizing Rural Economies, And Promoting Environmental Sustainability, Moving The Agricultural Sector Forward In A Meaningful Way.

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Other Details

• Paper id:

  IJSARTV11I12104428

• Published in:

  Volume: 11 Issue: 12 December 2025

• Publication Date:

  2025-12-09

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