Feature extraction in sensor plant disease datasets using reformed membership functions independent of class variables

Sensor-based datasets often have limited features because continuous sensor deployment is expensive and complex. This study aims to develop a Membership Function-based Feature Extraction (MFFE) technique that operates without dependency on class variables to enhance small-sized sensor-based plant datasets. The research utilizes two sensor-based tomato disease datasets - TomEBD and TPMD, which have been collected in real-time. To address the dataset imbalance, the KMeans-SMOTE technique is applied. Feature extraction is performed using reformed triangular and gaussian membership functions, where all parameters are computed solely from the training data to prevent information leakage and biased evaluation. The enhanced datasets are classified using two optimized models: Optimized Kernel Extreme Learning Machine (OKELM) and Optimized Radial Basis Function Neural Network (ORBFNN), both tuned using the Optuna framework. The proposed technique is further validated on eight benchmarking non-plant-based datasets. Among all models, the TMF-ORBFNN achieved the highest accuracy across both plant-disease and benchmark datasets. Further, statistical analysis using the Friedman test and post-hoc Bonferroni-Dunn test showed that TMF-ORBFNN performed significantly differently from its counterparts. The time complexity of the proposed approach has also been analysed. The proposed MFFE technique provides effective feature extraction in small, sensor-based datasets without class-variable dependency. Enhancing and classifying plant-disease datasets using the proposed TMF-ORBFNN model will help farmers take timely actions to prevent crop diseases and reduce pesticide use.