Fruit Disease Detection and Classification using Machine Learning and Deep Learning Techniques


  • Suvarna Eknath Pawar Professor, school of computing, MiT art, design and Technology University pune
  • Amruta V. Surana Associate professor , computer engineering dept Sinhgad institute of technology lonavala-410401
  • Pooja Sharma Associate Professor : School of Engineering and Technology, DY Patil University Ambi Pune
  • Ramachandra Pujeri Dean, school of computing, MIT Art, design and technology University Pune


Fruit Disease Detection, Feature extraction, Feature selection, Papaya fruit, Deep Learning Techniques, Classification


Agriculture has a substantial role in the Indian economy within the context of India. This is the primary and essential source of income for a significant portion of the human population. Therefore, it is important to enhance the output of fruits. Fruit diseases have a negative impact on the quality and overall condition of fruits. The primary cause of fruit illnesses is mostly attributed to fungal and bacterial pathogens. The timely identification of fruit diseases serves as a means to forecast and mitigate the occurrence of such diseases, hence resulting in cost savings for agricultural practitioners. The identification of an optimal approach for fruit disease detection is used as a proactive measure to mitigate the impact of fruit diseases during their first phases. Certain researchers have undertaken the task of developing a fruit disease identification system with the aim of safeguarding farmers' investments. The primary aim of this study is to conduct a comparative analysis of a deep learning classification approach in the context of fruit disease detection. This research we proposed an fruit disease detection and classification using hybrid machine learning and deep learning techniques. The various feature extraction and selection technique are utilized and ML and DL classification algorithms are applied on heterogeneous fruit dataset. In extensive experimental analysis the proposed hybrid CNN achieves highest 97.10 accuracy for all fruit image dataset.


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How to Cite

Pawar, S. E. ., V. Surana, A. ., Sharma, P. ., & Pujeri, R. . (2023). Fruit Disease Detection and Classification using Machine Learning and Deep Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 440–453. Retrieved from



Research Article