Simulation results demonstrate that identifying plant disease via a DCNN-based protocol is suitable and adaptable to other plants, providing high practical value.

This study introduces YOLO-AgriNet, a customized object detection model built on the YOLOv8 architecture, optimized for plant disease detection under tropical and low-resource conditions.

Researchers at Carnegie Mellon University, together with scientists at a conservation ranch in Montana, developed a method that trains machine learning models to detect invasive species more ...

A smart plant disease detection web application built with Streamlit and TensorFlow, designed to help farmers, researchers, and plant enthusiasts diagnose diseases from leaf images with high accuracy.

This study proposes an advanced method for plant disease detection utilizing a modified depthwise convolutional neural network (CNN) integrated with squeeze-and-excitation (SE) blocks and improved ...

This paper presents a framework leveraging Raspberry Pi for the detection and prevention of plant diseases, employing a Convolutional Neural Network (CNN) algorithm for image analysis. With a focus on ...

🌿 Plant Disease Detection (PDD) System AI-Powered Plant Disease Detection System Key Features • Architecture • Quick Start • Plants • Tech Stack • API ...

In recent decades, the maturation of computer vision technology has provided more possibilities for implementing plant disease detection. Nonetheless, detecting plant diseases is typically hindered by ...