Machine Vision Recognition System for Iceberg Lettuce Health Condition on Raspberry Pi 4b: A Mobile Net SSD v2 Inference Approach (original) (raw)

Machine Vision Recognition System for Iceberg Lettuce Health Condition

IJETER, 2020

Lettuce provides vitamin C, calcium, potassium, and folate. Within iceberg lettuce, the nutrients will help you fulfill the normal daily requirements for many vitamins and minerals. It is most commonly cultivated as a vegetable leaf, but sometimes for its stem and seeds. Lettuce is most widely used for salads, but it can also be used in other foods, such as soups, sandwiches, and wraps; it can be grilled too. Many farmers produce lettuces on the farm. Producing lettuces isn't that an easy task it requires manpower and hard work. People who buy lettuce don't have the skill to determine if the lettuce is healthy or have a disease, they just based only on the color of the lettuce. The study developed a system project that focuses on lettuce health recognition. The system determines if the lettuce is healthy or disease. It is based on machine vision using deep learning, it is connected to a microcontroller raspberry pi 4b. Lettuce health recognition has been done with an overall testing accuracy of 97.59%.

Classification of Tomato Using Deep Learning

2020

Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which helps in protecting them from developing deadly blood clots. A tomato classification approach is presented with a data set containing approximately 5,266 images with 7 species belonging to tomatoes. The Neural Network Algorithms (CNN), a deep learning technique applied widely in image recognition, is used for this task.

Smart mobile application to recognize tomato leaf diseases using Convolutional Neural Networks

The automatic identification and diagnosis of tomato leaves diseases are highly desired in field of agriculture information. Recently Deep Convolutional Neural networks (CNN) has made tremendous advances in many fields, close to computer vision such as classification, object detection, segmentation, achieving better accuracy than human-level perception. In spite of its tremendous advances in computer vision tasks, CNN face many challenges, such as computational burden and energy, to be used in mobile phone and embedded systems. In this study, we propose an efficient smart mobile application model based on deep CNN to recognize tomato leaf diseases. To build such application, our model has been inspired from MobileNet CNN model and can recognize the 10 most common types of Tomato leaf disease. Trained on tomato leafs dataset, to build our application 7176 images of tomato leaves are used in the smart mobile system, to perform a Tomato disease diagnostics.

Grapefruit Classification Using Deep Learning

2020

Abstract: Fruit has been recognized as a good source of vitamins and minerals, and for their role in preventing vitamin C and vitamin A deficiencies. People who eat fruit as part of an overall healthy diet generally have a reduced risk of chronic diseases. Fruit are important sources of many nutrients, including potassium, fiber, vitamin C and folate (folic acid). One of important types of fruit is Grapefruit . Grapefruit is a tropical citrus fruit known its sweet and somewhat sour taste. It's rich in nutrients, antioxidants and fiber, making it one of the healthiest citrus fruits you can eat. Research shows that it may have some powerful health benefits, including weight loss and a reduced risk of heart disease. In this paper we presented a system that recognize the two types of Grapefruit Pink and white based on deep learning using python on colab editor . This system may help people to automate their factories , restaurants or anything else need to classify these two types fo...

IJERT-Automatic Fruit and Vegetable Detection and Disease Identification System

International Journal of Engineering Research and Technology (IJERT), 2020

https://www.ijert.org/automatic-fruit-and-vegetable-detection-and-disease-identification-system https://www.ijert.org/research/automatic-fruit-and-vegetable-detection-and-disease-identification-system-IJERTCONV8IS15017.pdf Nowadays in agriculture industry exporting the fruits to other countries in bulk quantities is a difficult task. In this field farmers need manual inspection. Our system helps the farmers to pack their fruits as soon as possible by detecting the fruits and vegetable and identifying the disease this helps the farmers to save their time and they can delivery fruits and vegetable as soon as possible. We use CNN algorithm for fruits and vegetable detection and disease identification. Using neural network the image is segmented which is followed by extraction of some features from the segmented image finally fruits and vegetable image is identified and labeled.

Design a mobile application to detect tomato plant diseases based on deep learning

Bulletin of Electrical Engineering and Informatics, 2022

Plant diseases consider the most dominant matter for farmers' concerns because the operation of discovering and dealing with them requires accuracy, experience, and time. Therefore, this paper proposes an approach to classify seven varieties of tomato diseases using deep learning models. A dataset of 10448 images from PlantVillage and google utilize to train the deep learning (CNN models). The trained models proved their ability to classify with high accuracy, as the highest testing accuracy reached 95.71% for the proposed model for 50 epochs only. The resulted best model is published to a mobile application using the android studio platform, this application enables the farmer to classify plant diseases accurately and easily. The proposed model and mobile application could be extended to classify as many plant diseases as possible.