Unlocking genetic code of crop-damaging fungus paves way for better disease control

Researchers from CSIRO, Australia's national science agency, have unlocked the most detailed genetic blueprint yet of a major ...
TechAnnouncer

Mastering Crop Scouting with Drones: A Comprehensive Guide for Modern Agriculture

Here’s a quick checklist: Physical Inspection: Check the drone’s body, arms, and landing gear for any damage. Look closely at ...

Wikipedia volunteers spent years cataloging AI tells. Now there’s a plugin to avoid them.

The source material is a guide from WikiProject AI Cleanup, a group of Wikipedia editors who have been hunting AI-generated ...

Satellites and AI can help tackle critical invasive species problem

Satellite imagery and artificial intelligence can detect with high accuracy two invasive weed species in Australia, posing a ...
Devdiscourse

From crop monitoring to AI analytics, drones redefine smart agriculture

The transformation is documented in the study A Review of Drones in Smart Agriculture: Issues, Models, Trends, and Challenges ...
Futurism

Malware Is Now Using AI to Rewrite Its Own Code to Avoid Detection

Researchers at Google’s Threat Intelligence Group (GTIG) have discovered that hackers are creating malware that can harness the power of large language models (LLMs) to rewrite itself on the fly. An ...
The New York Times

What We Can Learn From Brain Organoids

Lab-grown “reductionist replicas” of the human brain are helping scientists understand fetal development and cognitive disorders, including autism. But ethical questions loom. Brain organoids, which ...
Hosted on MSN

How to Read Deep Learning Code – A Beginner’s Guide

Learn how to effectively read and understand deep learning code with this beginner-friendly guide. Break down complex scripts and get comfortable navigating AI projects step by step. #DeepLearning ...
Frontiers

Smart intercropping system to detect leaf disease using hyperspectral imaging and hybrid deep learning for precision agriculture

Introduction: The rapid growth of the global population and intensive agricultural activities has posed serious environmental challenges. In response, there is an increasing demand for sustainable ...
IEEE

Deep Learning-Driven Mobile Application for Rice Crop Disease Detection Using CNN

Abstract: This study presents a deep learning-based mobile app for rice crop disease detection using a MobileNetV2-based CNN model trained on a dataset of 4,700 rice leaf images. The app accurately ...
IEEE

Soil Type Detection and Crop Prediction Using Deep Learning Method CNN

Abstract: The study presents a novel use of Convolutional Neural Network (CNN) algorithms for crop prediction and soil type classification. Soil type classification is crucial for agricultural ...