Mehra, T, Kumar, V, Gupta, P (2016) Maturity and disease detection in tomato using computer vision. In 2016 4th International Conference on Parallel, Distributed and Grid Computing, PDGC 2016. Distributed and Grid Computing (PDGC. https://doi.org/10.1109/PDGC.2016.7913228 Mendoza F, Dejmek...
Outside of competition setting, if we expect our model to be used in practical applications, it is important to explain why the model classified a given image. We explored a few examples using an explainable AI technique called Gradient-weighted Class Activation Map (Grad-CAM) to highlight the...
Increased efficiency in crop monitoring and disease detection. Improved yield potential through timely interventions. Enhanced decision-making capabilities for growers, leading to optimized agricultural practices. 0% Increase in Scouting Efficiency Compared to Traditional Methods 0x More Acres Scouted per Day...
Inaddition to reducing waste, using Deep learningtechnologies can increase quality and speed up marketaccess for farmers. Here, we summarize recent cropdisease detection research papers. Multiple deep learningalgorithms demonstrate the current solutions for differentcrop disease diagnoses in this research. ...
AI Challenger 2018 农作物病害检测. Contribute to Cooper111/Crop-Disease-Detection development by creating an account on GitHub.
A team of researchers has turned the keen eye of AI toward agriculture, using deep learning algorithms to help detect crop disease before it spreads.
Disease Detection: Identify a wide range of crop diseases with high accuracy using just a photo. Easy to Use: Designed with a user-friendly interface for farmers with varying levels of literacy. Real-Time Insights: Offers immediate feedback, allowing for timely intervention and treatment of crops...
Boost crop yields with MapMyCrop's AI-powered satellite Crop monitoring software. Access real-time data on soil health, water stress, and disease detection, delivered via WhatsApp in local languages. Join 3 million global users in achieving sustainable a
This research presents a comprehensive review of the state-of-the-art DL architectures integrated with IoT-based systems applied to plant pest and disease detection (PPDD) by investigating different potential approaches that have been employed using DL and IoT up to the year 2024 to address ...
Visual large language model for wheat disease diagnosis in the wild 2024, Computers and Electronics in Agriculture Show abstract The Impact of the EU’s AI Act and Data Act on Digital Farming Technologies 2025, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intel...