Affiliations: University School of Information, Communication & Technology, GGSIPU, New Delhi, India
Correspondence:
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Corresponding author: Ayushi Gupta, University School of Information, Communication & Technology, GGSIPU, Sector 16-C Dwarka, New Delhi, India. E-mail: ayushi.20616490021@ipu.ac.in.
Abstract: PURPOSE: Crop diseases can cause significant reductions in yield, subsequently impacting a country’s economy. The current research is concentrated on detecting diseases in three specific crops – tomatoes, soybeans, and mushrooms, using a real-time dataset collected for tomatoes and two publicly accessible datasets for the other crops. The primary emphasis is on employing datasets with exclusively categorical attributes, which poses a notable challenge to the research community. METHODS: After applying label encoding to the attributes, the datasets undergo four distinct preprocessing techniques to address missing values. Following this, the SMOTE-N technique is employed to tackle class imbalance. Subsequently, the pre-processed datasets are subjected to classification using three ensemble methods: bagging, boosting, and voting. To further refine the classification process, the metaheuristic Ant Lion Optimizer (ALO) is utilized for hyper-parameter tuning. RESULTS: This comprehensive approach results in the evaluation of twelve distinct models. The top two performers are then subjected to further validation using ten standard categorical datasets. The findings demonstrate that the hybrid model II-SN-OXGB, surpasses all other models as well as the current state-of-the-art in terms of classification accuracy across all thirteen categorical datasets. II utilizes the Random Forest classifier to iteratively impute missing feature values, employing a nearest features strategy. Meanwhile, SMOTE-N (SN) serves as an oversampling technique particularly for categorical attributes, again utilizing nearest neighbors. Optimized (using ALO) Xtreme Gradient Boosting OXGB, sequentially trains multiple decision trees, with each tree correcting errors from its predecessor. CONCLUSION: Consequently, the model II-SN-OXGB emerges as the optimal choice for addressing classification challenges in categorical datasets. Applying the II-SN-OXGB model to crop datasets can significantly enhance disease detection which in turn, enables the farmers to take timely and appropriate measures to prevent yield losses and mitigate the economic impact of crop diseases.
