Detection Of Pesticides in Fruits and Vegetables Using IOT and ML

The widespread use of pesticides in agriculture has led to significant concerns about food safety and public health. Conventional methods for pesticide residue detection are often time-consuming, expensive, and require complex laboratory infrastructure. To address these limitations, this project proposes an integrated system leveraging Internet of Things (IoT) and Machine Learning (ML) technologies for real-time detection of pesticide residues in fruits and vegetables. The proposed system employs smart sensors capable of detecting chemical signatures associated with common pesticides. These sensors are embedded into a portable IoT device that collects and transmits data wirelessly to a cloud-based platform. The collected data is then processed using machine learning algorithms trained on labeled datasets to accurately identify the presence and concentration of pesticide residues. The ML model utilizes techniques such as Support Vector Machines (SVM), Random Forest, and Neural Networks to achieve high accuracy in classification and prediction tasks. The system provides real-time feedback via a user- friendly mobile or web application, enabling consumers, vendors, and regulatory bodies to make informed decisions about food safety. This approach not only enhances the efficiency and accessibility of pesticide detection but also contributes to the development of a smarter and safer food supply chain. The integration of IoT and ML presents a scalable, cost- effective, and non-invasive solution for addressing one of the most pressing challenges in modern agriculture and food security.