Engineering Commons^™

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

A Study On Image Processing Techniques And Deep Learning Techniques For Insect Identification, Vinita Abhishek Gupta, M.V. Padmavati, Ravi R. Saxena, Pawan Kumar Patnaik, Raunak Kumar Tamrakar

Karbala International Journal of Modern Science

Automatic identification of insects and diseases has attracted researchers for the last few years. Researchers have suggested several algorithms to get around the problems of manually identifying insects and pests. Image processing techniques and deep convolution neural networks can overcome the challenges of manual insect identification and classification. This work focused on optimizing and assessing deep convolutional neural networks for insect identification. AlexNet, MobileNetv2, ResNet-50, ResNet-101, GoogleNet, InceptionV3, SqueezeNet, ShuffleNet, DenseNet201, VGG-16 and VGG-19 are the architectures evaluated on three different datasets. In our experiments, DenseNet 201 performed well with the highest test accuracy. Regarding training time, AlexNet performed well, …

Smart-Insect Monitoring System Integration And Interaction Via Ai Cloud Deployment And Gpt, Ahmed Moustafa

Computer Science and Computer Engineering Undergraduate Honors Theses

The Insect Detection Server was developed to explore the deployment and integration of an Artificial Intelligence model for Computer Vision in the context of insect detection. The model was developed to accurately identify insects from images taken by camera systems installed on farms. The goal is to integrate the model into an easily accessible, cloud-based application that allows farmers to analyze automatically uploaded images containing groups of insects found on their farms. The application returns the bounding boxes and the detected classes of insects whenever an image is captured on-site, enabling farmers to take appropriate actions to address the issue …