Authors : V. S. R. K. Prasad G.; Jamisetti Balu; Sohail Khader Shaik; Gayathri Damarla; Abdul Razaq Shaik; Meghana Polisetty

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/y9uhyrux

DOI : https://doi.org/10.38124/ijisrt/26jun1243

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Spoilage of food is one of the major issues that affects human health as well as the economy in the form of food wastage. A large number of spoiled food products cannot be recognized with the naked eye; therefore, the process of quality assessment is tedious and not reliable. The need for the development of a smart system that can efficiently sense the spoilage of food in real time is therefore required. The objective of the present work is the development of an Internet of Thingsbased system that can efficiently sense the quality of food in real time. The gas sensor and temperature sensor will be used to collect the information regarding the condition of the food, while the camera will be used to collect the image of the food item. The collected data is then processed with the help of a microcontroller, and it is analyzed with the help of a deep learning model, which is based on a convolutional neural network. The classification of the food is then done as fresh or spoiled. The system is based on the combined results of both sensor and image data. The results obtained from testing the system show an accuracy of 95.83 percent, thereby showing its efficiency in detecting spoiled food. The proposed system helps in the early detection of spoiled food, minimizes wastage, and is highly efficient in terms of safety.

Keywords : Plant Disease Detection, Tea Leaf Classification, Ensemble Learning, Deep Learning, Image Analysis and Precision Agriculture.

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