Classification Learning Assisted Biosensor Data Analysis for Preemptive Plant Disease Detection

In the agricultural sector, identifying plant diseases is crucial as they hamper plant's robustness and health, which play a vital role in productivity. Early detection allows farmers to take proper measurements save crops from complete failure. Biosensor applications production monitoring improve yield through definitive recommendations improved practices. Fluorescence-based assays, colorimetric biosensors, surface plasmon resonance-based biosensors are most commonly used for pathogen detection. Plant disease prime biosensor application preventing seasonal cultural defects raising crops. The sensor data analysis ensures reliable processing distinguishable features disease, wherein discrete information handled with error. This article introduces Preemptive Classification using Discrete Data (PC-DD) technique resolve this issue. requires partial series probabilistic substitution rate. process, classification performed random forest based on two combinations: series, difference, probability. probability identical observed previous iteration. unidentical classified under difference that individual classification. process progressive until performed, alterations adaptable different input data. Therefore, proposed technique's performance validated metrics accuracy, ratio, time, classifications, factor.