Corn stalk disease classification using random forest combination of extraction features

Abstract

Agriculture is one of the essential sectors for human livelihood sustainability. The primary crop cultivated worldwide is corn. Unfortunately, it is often susceptible to various diseases that can threaten crop yields and food availability. One vulnerable part of the corn plant to bacterial and viral infections is the corn stalk. The corn stalk disease is a critical issue that can impact the growth and yield of the crop. It serves as the primary support system for the plant and, is crucial for maintaining the stability and productivity of corn plants. Therefore, a preventive effort to maintain plant health and enhance agricultural productivity for initial detection is essential. Technologies in data mining for digital image classification are implemented. To classify corn stalk diseases, this study suggests machine learning strategies such as Random Forest, Support Vector Machine, and K-Nearest Neighbor (K-NN). Furthermore, a mix of LBP (Nearby Double Example) and HSV (Tone Immersion Worth) highlight extraction is utilized in this examination. A dataset of digital images of corn plants containing 750 records with 5 classes is assessed. Results show the highest accuracy that the Random Forest algorithm has 82%, and AUC is 96.2%. F1-Score, Precision, and the Recall are 82%.