Plant Species Health Detection Using Artificial Intelligence

Abstract

Plants health detection is essential in maintaining agricultural output and sustainable environment. This study examines the utilization of Artificial Intelligence (AI) algorithms in automating plant health analysis. the system scans plant images in search of diseases, nutrients, and stress levels. The proposed solution integrates computer vision and machine learning models, which are trained across different datasets and include both samples of healthy and ill plants. The AI detection system enhances early diagnosis such that intervention is done on time and crops are not lost. The research shows the accuracy of AI in measuring plant health, providing a valuable and scalable procedure for both scientists and farmers. Our system makes use of Convolutional Neural Networks and deep learning techniques trained on huge sets of images of plants with both healthy and sick examples. By analyzing leaf shapes, color patterns, and distorted shapes, the AI model can accurately classify plant conditions, including diseases such as bacterial infections, fungal infestation, and starvation for nutrients. The system is designed for early plant disease detection, allowing farmers to intervene in time and minimize financial losses. Future developments involve the integration of Internet of Things (IoT) sensors for real-time sensing and a broader range of plant species for the model.