An IoT-Based Pest Detection and Alert System for Farmers Using CNN-LSTM Approach

Insects and other pests are a major cause of agricultural economic loss in India. Consequently, farmers heavily rely on pesticides to combat weeds, insects, and plant diseases. Misusing pesticides is bad for people and the economy. This research study proposes a novel method for controlling pests that takes use of image processing and the Internet of Things in order to cut down on the application of harmful chemicals. Insects can be detected with the help of an infrared sensor (PIR) in the suggested system. To verify the pest's existence in the field, photos are captured using image processing. After receiving an input image, the suggested process includes four steps: preprocessing, segmentation, feature extraction, and model performance evaluation. Processing in grayscale and improving individual features are among the preprocessing methods employed. It employs a histogram to perform the segmentation. Extraction of Region of Interest (ROI), Extraction of Detailed Image, Extraction of ORB Key Points, and Extraction of Feature Descriptor are all used in the process of feature extraction. CNN, LSTM, and CNN-LSTM make up the performance evaluation suite. When compared to those other two tried-and-true approaches, the proposed CNN-LSTM strategy fares quite well.