pest-detection

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.

A machine learning-based crop health monitoring system that detects plant pests and diseases from leaf images and provides accurate diagnosis with treatment recommendations to support farmers in protecting their crops.

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.

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.

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.

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.

Identifies crop pests from a photo in seconds and gives treatment advice in AZ/EN/RU. The model runs on the device, so it works with no signal.

agrio — independent third-party profile of a public API surface, by API Evangelist. Agrio is a precision plant protection solution that helps growers and crop advisors forecast, identify, and treat plant diseases, pests, and nutrient deficiencies. With Agrio APIs, developers can access AI-powered plant disease diagnosis from images, crop advisory d

Autonomous Agricultural Rover & Base Station — Real-time YOLOv8 pest/disease detection, AES-encrypted NRF24L01 telemetry, live GPS grid tracking, and precision spray control.

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

🌾 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.

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