Chili crop disease prediction using machine learning algorithms

Abstract

Crop diseases are a major cause of reduced productivity in India, with farmers often struggling to identify and control them. Consequently, the development of advanced techniques for early disease detection is crucial for minimizing losses. This study investigates the performance of various Machine Learning (ML) algorithms, including Random Forest (RF), AdaBoost, Gradient Boosting (GB), and Multi-Layer Perceptron (MLP), for predicting diseases in chili crops based on images. The primary objective is to identify the most accurate model for chili crop disease prediction. A novel dataset, the Real Chili Crop Field Image Dataset, comprising approximately 1157 images across 5 distinct classes, is employed for this purpose. The experimental results demonstrate that the RF and GB algorithms achieve accuracies of 96% and 94%, respectively. Importantly, the study focuses on the Real Chili Crop Field Image Dataset, which offers significant advantages in terms of real-world applicability due to its development in natural, non-controlled environments. The methodology is further enhanced by employing popular and diverse feature extraction methods, such as Haralick and Hu moments, and improving the results using the Random Forest classification algorithm.