Utility System for Tomato Infections Detection and Classific | 92114
International Research Journals

International Research Journal of Plant Science

All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.

Utility System for Tomato Infections Detection and Classification Using Deep Learning with IoT


R. Ramya*, P. Kumar, M. Senthil Kumar, V. Sounthar Raj and M. Mohamed Faizal

Agriculture is indisputably the backbone of our nation. India is the second-largest producer of agricultural products globally. In terms of people to feed, Indian agriculture lags well behind other countries in terms of per hectare production in practically all crops. Plant infections cause major economic and production losses and curtail agricultural production quantity and quality. Producers need to monitor their plants regularly and observe any primary symptoms to prevent plant sickness at a low cost and save a significant part of the production. In recent days, technology has played a vital role in all research fields. So, the help of technology is used to detect plant infection. In India, technology-based modern agriculture is the most required to make more profit in every part of agriculture. Thus, the application of technology in agriculture, such as precision agriculture, may assist enhance productivity, improve the condition of Indian farmers, and preserve their products. Thus, the overall progress of production is obtained. Detection of infections in crop management is peremptory for agriculture to be sustainable. However, because to the cluttered background in today's agricultural economy, automated crop infection diagnosis and prediction is a major difficulty. The Internet of Things (IoT) and deep learning have played a significant role in the recent decade, gathering a tremendous amount of contextual data to recognise agricultural infections. This paper describes a real-time technique for detecting tomato leaf infection based on deep convolutional neural networks such as mobileNet and ResNet CNN models. Tuning the hyper-parameters and altering the pooling combinations on a system enhances deep neural network performance. A neural compute stick comprised of dedicated CNN hardware blocks is used to deploy the pre-trained deep CNN model onto a PIC microcontroller. TTL is used to connect the output from the MATLAB software in the personal computer and PIC microcontroller. The PIC microcontroller is programmed with an embedded C programme, and the ESP8266 IoT module is used to transfer data from the microcontroller to the cloud server, where it is displayed on a web page. The deep learning model obtains more accuracy in the detection of leaf infections, indicating the practicality of this technology and the treatment of infected leaf infections.

Share this article