Open-source pest detection system using YOLOv5 and the IP102 dataset.

it is a 🌿 real-time pest detection system for urban gardens. It uses the lightweight 🤖 YOLOv8 Nano model to identify pests like 🐜 aphids and 🦟 fruit flies, optimized for edge devices like the 🍃 Raspberry Pi 4.

AI-powered agricultural assistance platform for farmers. Features pest detection (38 disease classes), crop recommendations, market prices, weather integration, and farming guidance. Built with React Native, Node.js, PostgreSQL, and TensorFlow.

AI-powered smart agricultural IoT monitoring system using Raspberry Pi sensors and Llama 3.2 LLM for offline crop management. Provides real-time environmental data analysis and natural language farming insights without internet dependency.

🌱 Diagnóstico inteligente de plagas para agricultores de Santa Cruz, Bolivia — Gemini Vision + Open-Meteo · Build With AI 2026

AI-Powered Agricultural Intelligence Platform | Crop yield prediction, pest detection, soil analysis & market insights for Indian farmers | Next.js 15 + Google Gemini + TypeScript

Scientific initiation project aimed at understanding the counting and classification of cochineals in forage palm rackets.

AI Farmer Assistant is a smart agriculture support system that helps farmers with crop recommendations, pest detection, weather updates, soil analysis, fertilizer guidance, and AI-powered farming assistance. Built using Python, HTML, CSS, JavaScript, and AI technologies to promote smart and sustainable farming solutions.

Advanced Channel-Enhanced Multi-Scale Pest Detection Network (CMPestNet) for crop-specific and cross-crop pest identification. Outperforms SOTA on Jute17, Pest24, and IP102 datasets.

This repository contains a Jupyter notebook that demonstrates how to fine-tune the YOLOv11 object detection model on the Agricultural Pests Dataset using Google Colab.

Agrio is a precision plant protection solution that helps growers and crop advisors forecast, identify, and treat plant diseases, pests, and nutrient deficiencies.

🌾 KrishiSahayak – A digital agricultural assistant that provides crop recommendations, pest & disease detection, soil health tips, and a multilingual chatbot to support farmers with timely and localized guidance.

Comparative computer vision study for agricultural pest detection using classical ML, Faster R-CNN, RetinaNet, and YOLO.

AI-powered insect identification system for farmers * Identifies pests vs pollinators from photos * 7 insect classes | 85%+ accuracy | 5 languages * IPM-based recommendations | Free APIs * Supports UN SDGs: 2, 12, 13, 15

ML & AI algorithm for the FarmIntel web interface

Eco-friendly pest management system combining computer vision for pest detection and vibrational signal disruption for sustainable, pesticide-free agriculture.

An integrated deep learning framework designed to detect and predict agricultural pest infestations using image classification and object detection to improve crop management and minimize pesticide usage.

COMP9517 group project: multi-model insect detection on AgroPest-12 using Traditional CV, YOLO, Faster R-CNN, and RT-DETR with a unified preprocessing pipeline.

Reproducibility companion for density-stratified YOLOv11 stored-product pest detection and generalization experiments.

🌊 Streamline water management with Aquanex-Server, a robust tool for monitoring and analyzing aquatic data efficiently.