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Spot-out fruit fly algorithm with simulated annealing optimized SVM for detecting tomato plant diseases

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Abstract

Crop diseases are a huge threat to food security, yet timely detection is a difficult task due to the absence of infrastructure in various regions of the world. In agriculture, the detection of disease in plants is complex because farmers must often evaluate whether the crop that are harvesting appears good enough. It is crucial to treat this seriously because it may result to major effects on plants, affecting product characteristics, quantity, or overall productivity. Plant illnesses produce outbreaks of disease on a systematic interval, resulting in large-scale fatalities and a substantial economic impact. Early and precise tools for diagnosing plant diseases are essential for robust plant production and for reducing both qualitative and quantitative losses in crop yield. Cutting-edge and creative data analysis technologies significantly aid in the accurate and precise identification of diseases. Among all the crops, tomato plants are widely grown and required in all parts of the world. Given all the above challenges, this study seeks to recognize tomato plant diseases in an accurate and timely manner. In this paper, a multi-objective hybrid fruit fly optimization algorithm that relies on simulated annealing optimized SVM is proposed to identify tomato plant diseases at an earlier stage in an accurate manner avoiding global optimization problems. The hybridization of simulated annealing with FOA helps in reducing the hyperparameter problems. The proposed methodology was tested and experimented extensively and the results enlightened that the proposed methodology achieved 91.1% accuracy and reliability and the experimental observations also indicated that the suggested method overcomes the drawbacks of the current algorithms. In addition, the operational efficiency of the proposed system was measured on statistical parameters like accuracy (91.1%), sensitivity (96.7%), precision (91.8%), specificity (91.2%), and F1-score (94.5%). Also, a comparison analysis with existing algorithms like DT, RF, KNN, and K-means with SVM was also performed, and overall, it was concluded that proposed methodology is having high methodological approach for diagnosing crop diseases.

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Data availability

The data used in the research paper in form of data set are: Plant Village data set. The data set is available at: https://www.kaggle.com/datasets/emmarex/plantdisease.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Dr.MGR Educational and Research Institute, Chennai, 600095, India

    E. Gangadevi & R. Shoba Rani

  2. Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, 411016, India

    Rajesh Kumar Dhanaraj

  3. Graduate School, Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Viet Nam

    Anand Nayyar

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Gangadevi, E., Rani, R.S., Dhanaraj, R.K. et al. Spot-out fruit fly algorithm with simulated annealing optimized SVM for detecting tomato plant diseases. Neural Comput & Applic 36, 4349–4375 (2024). https://doi.org/10.1007/s00521-023-09295-1

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