Comparative Study of ML Techniques for Classification of Crop Pests

Crop pests pose a great threat to global food security; thus, the best pest prevention measures must be implemented. By using different machine learning (ML) techniques to perform crop pest classification, this research provides ways to improve the accuracy and speed of identifying pests in agricultural sectors. Conventional methods for identifying pests frequently depend on manual observation, which is tedious, error-prone, and labor-intensive. On the other hand, machine learning (ML) presents an effective way to automate this procedure by using sophisticated techniques to analyze massive data sets and produce precise predictions. The study applies a variety of machine learning approaches, such as Random Forests, K-Nearest Neighbor, and Naive Bayes, to classify agricultural pests according to features that have been extracted from images. For model training and validation, an extensive collection of high-resolution images of different agricultural pests taken in a range of environmental settings is used. Metrics like accuracy are used to determine how well the machine learning models perform. The potential of machine learning approaches to revolutionize pest management in agriculture is evident from the results, which indicate how accurately they can identify and classify agricultural pests. The suggested method improves the overall effectiveness of pest management procedures and drastically reduces the time and effort required to identify pests. Ultimately, this research promotes more resilient and productive farming systems by supporting efforts to develop sustainable and technologically advanced solutions for addressing agricultural difficulties. The results demonstrate the potential of machine learning (ML) as an invaluable tool for farmers, agronomists, and policymakers, encouraging a proactive and data-driven approach to pest management in contemporary agriculture.