Exploring Chili Plant Health: A Comprehensive Study Using IoT Sensors and Machine Learning Classifiers

Red chili, scientifically known as "Capsicum annuum," belongs to the Solanaceae family. It is extensively utilized in various cuisines worldwide to enhance flavor and impart heat to dishes. Moreover, red chili exhibits medicinal properties such as pain relief, anti-inflammatory effects, a metabolism boost, cardiovascular health benefits, and antioxidant properties. The primary objective of this research paper is to identify specific diseases affecting different instances of chili plants. We will analyze this through IoT sensors to determine which soil is optimal for chili cultivation. For this research, we created our dataset by collecting pictures of various specific diseases. The dataset comprises five features: Bacterial Spot, Powdery Mildew, Anthracnose, Phytophthora Root Rot, and Fusarium Wilt. In this study, a machine learning classifier was employed to detect chili plant diseases. Additionally, we identified various types of diseases in chili plants and evaluated their overall health. Experimental observations reveal that a Convolutional Neural Network (CNN) performs well compared to other deep learning classifiers. The training accuracy of CNN is 98.2%, and the testing accuracy is 96.7%. The minimized training and testing errors demonstrate that the model effectively handles new or unseen data. We have compared our proposed model to the state of the art and found that the proposed model performs well.