Ai Challenger 2018 Competitions 农作物病害检测

AI Challenger 2018 农作物病害检测

Plant diseases causes many significant damages and losses in crops around the world. Some suitable measures on disease identification should be introduced to prevent damages and minimize losses. Early Detection of Disease helps in increasing the crop productivity as well as in minimizing expense. Technical approaches using machine learning and computer vision are actively researched to achieve intelligence farming by early detection on plant disease. The accuracy of object detection and recognition systems has been drastically improved by the recent development in Deep Neural Networks. By using these systems and implementation of computer vision and machine learning techniques, plant diseases can be detected. Here we have used transfer learning based approach to diagnose diseases of different plants using its images captured by camera devices either drone or smartphone. Our goal is to build a market oriented product for Plant Disease Detection, a smartphone app compatible with both smartphone camera and drone camera. The target group of the user is those who request a quick diagnosis on common leaf disease at any time of the day i.e. Farmers, agricultural industries, agricultural consultants and Government Agencies & Departments.

🌱 This project aims to automate plant health monitoring using computer vision and deep learning. It focuses on accurate disease detection and classification in plants through rigorous data preprocessing and robust model selection.

Plants health monitoring through iot and plants disease detection using machine learning in flutter

Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves. This paper introduces an efficient approach to identify healthy and diseased or an infected leaf using image processing and machine learning techniques. Various diseases damage the chlorophyll of leaves and affect with brown or black marks on the leaf area. These can be detected using image prepossessing, image segmentation. Support Vector Machine (SVM) is one of the machine learning algorithms is used for classification. The Convolutional Neural Network (CNN) resulted in a improved accuracy of recognition compared to the SVM approach.

Implemented Machine Learning and Artificial Intelligence model to detect the different disease on plants using the images.

Disease detection in plants using Deep learning and Transfer learning.

A Smart-Farming solution for farmers to ease the process of farming with the help of IOT and ML . It provides the farmers a way to monitor their farms with IOT smart solutions and early plants disease detection through ML.

Plant Disease Detection using convolutional neural network. Our model can easily predict the disease of plants like Potato , Tomato , Pepper Bel and many more in the upcoming version.

A deep learning model for disease detection in tomato plants using Deep Convolutional Generative Adversarial Network (DCGAN) as data augmentation technique.

Plants Disease Detection using ML

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Green Stems is an application built with AI capabilities for disease detection, health detection, flower species identification and soil identification for better care and nourishment of our plants.

AI disease detection and prediction for humans, plants, and animals. Complete ML project with custom training, offline operation, no API keys. Detect diseases from images using deep learning and computer vision. Open-source disease detection system for healthcare, agriculture, and veterinary applications. Full code and deployment guides.

The project is based on the leaf disease detection using cnn model and provide remedies for the disease plants.

Plant Disease Detection is an innovative machine learning project that harnesses the power of Convolutional Neural Networks (CNN) and deep learning techniques to identify and classify diseases in plants.

Crop Disease Detection System: A machine learning-based web application that allows users to upload images of plants, detects potential diseases using YOLOv8, and provides detailed disease information, prevention tips, and product recommendations from Amazon India. Built with Streamlit for an interactive experience.

A web and Android-based plant disease detection system using AI and Computer Vision. Users can upload or capture images of plants to get instant disease diagnosis along with cause, treatment, and prevention all in English, Hindi, or Marathi.

Agriculture is the one of biggest factor in every countries economy and the major problem facing by many agriculturalists is detection of diseases in the early stage and using correct pesticides and avoid major losses during cultivation. In my research work a dataset with 14 different types of diseases of 6 different types of plants were used for early detection in diseases in plant leaves. Deep learning models like GoogleNet, ResNet101 and MobileNet are used for classifying the type of diseases in various plant leaves. Evaluation metrics like precision, recall and f1-score were used for analyzing the results of the model.

This repo is for detecting plant diseases using deep learning. Plants provide over 80% of the food consumed by humans and are the primary source of nutrition for livestock. However, 1.3 billion tons every year of food are wasted, and there are 800 million hungry people around the world. Food waste is a huge source of greenhouse emissions and wasted natural resources. Thus, this project is to help reduce these numbers and secure more food for those hungry people at zero

PlanteD is an innovative plant leaf disease detection app developed using the powerful Flutter framework, combining the prowess of Artificial Intelligence (AI) and Machine Learning (ML). Designed for plant enthusiasts, gardeners, and farmers, PlanteD revolutionizes the way we identify and combat leaf diseases, ensuring healthier plants.

Convolutional neural network models were developed to perform plant disease detection and diagnosis using simple leaves images of healthy and diseased plants, through deep learning methodologies. Training of the models was performed with the use of an open database of 19,721 images, containing different plants in a set of 15 distinct classes of [plant, disease] combinations, including healthy plants. Several model architectures were trained, with the best performance reaching a 93.6% success rate in identifying the corresponding [plant, disease] combination (or healthy plant). The significantly high success rate makes the model a very useful advisory or early warning tool, and an approach that could be further expanded to support an integrated plant disease identification system to operate in real cultivation conditions.

The Cotton Disease Detection System is an AI-powered web application that helps farmers and agricultural experts identify diseases in cotton plants through image analysis.

Using Pretrained Models such as VGG19, Inception etc models on the high amount of data

An object detection model using PyTorch to detect leaf disease in Strawberry Plants

"Plant Disease Detection" is a project that utilizes the ResNet-50 deep learning model to predict potential diseases in plants by analyzing their leaves. The model has been trained on various types of plants, including potato, tomato, corn, and more, to ensure a wide range of disease detection capabilities.

This app uses a deep learning model built with PyTorch to detect diseases in plants based on images of their leaves. Currently, the app supports disease detection for maize (corn), but we plan to expand to more plants in the future.

This repository contains code and resources for an end-to-end system designed to automate the detection and severity estimation of diseases in tomato plants.

It is observed that a lot of crop gets wasted every year due to the illness spreading (reasons can be pests, bacterial or fungal infections, etc.) in the farms. Farmers face huge losses because of the lack of a handy and economical technology that can detect such diseases in the initial stages of occurrence. This system can prove a remedy to this issue by developing a prototype that recognizes the same at an early stage and thence providing them a way to cure it using appropriate pesticides, insecticides, etc. �The aim is to build a deployable system for plants’ disease detection by training a Convolutional Neural Network model that traces a particular disease in a particular plant species. It detects the plant disease by scanning the leaves of the species as it is trained for those illness occurring on the leaves.Leaf plays a major role in providing important information related to the health of a plant. This method is based on a technique that scans the leaf sample of the infected plant. These scanned samples will then be processed and the model will be trained to classify the diseases accordingly�. �