Tomato Research Project


Authors : Tharmithan Nanthakumar; Ishalini Senthamilpalan; Nirushan Pathmarajan; Kaviseshan Santhirapiragasam; Aruna Ishara Gamage; Jeewaka Perera

Volume/Issue : Volume 8 - 2023, Issue 10 - October

Google Scholar : https://tinyurl.com/3mkp3ar2

Scribd : https://tinyurl.com/yedpznt6

DOI : https://doi.org/10.5281/zenodo.10066534

Abstract : This research aimed to harness the power of machine learning techniques for comprehensive analysis of tomato plants in the context of disease identification, seed type prediction, and NPK estimation. Deep learning models, including ResNet and VG19, were employed for disease identification and NPK prediction, while an Artificial Neural Network (ANN) model was utilized for seed type prediction. The results revealed that ResNet achieved a superior overall accuracy of approximately 0.71 compared to MobileNet for disease identification. The VG19 model showcased impressive accuracy with 0.95 overall accuracy for NPK prediction, while the ANN model achieved an accuracy of 0.37. These findings highlight the potential of deep learning models and transfer learning for accurate disease identification and NPK estimation in tomato plants. The research contributes valuable insights and guidance for the development of intelligent systems to enhance tomato cultivation practices and empower farmers with effective tools for decision-making.

Keywords : Image Processing, Image Classification, Machine Learning , Deep Learning , Computer Vision , Regression.

This research aimed to harness the power of machine learning techniques for comprehensive analysis of tomato plants in the context of disease identification, seed type prediction, and NPK estimation. Deep learning models, including ResNet and VG19, were employed for disease identification and NPK prediction, while an Artificial Neural Network (ANN) model was utilized for seed type prediction. The results revealed that ResNet achieved a superior overall accuracy of approximately 0.71 compared to MobileNet for disease identification. The VG19 model showcased impressive accuracy with 0.95 overall accuracy for NPK prediction, while the ANN model achieved an accuracy of 0.37. These findings highlight the potential of deep learning models and transfer learning for accurate disease identification and NPK estimation in tomato plants. The research contributes valuable insights and guidance for the development of intelligent systems to enhance tomato cultivation practices and empower farmers with effective tools for decision-making.

Keywords : Image Processing, Image Classification, Machine Learning , Deep Learning , Computer Vision , Regression.

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