AI-Based Plant Disease Detection and Recommendation System

@pkundu25

I am an experienced AI Developer. Your job caught my eye and looks to be quite interesting to me as I developed Fetal brain abnormality detection using transfer learning in recent past. I am well conversant with Generative AI and hands-on experience in developing AI applications using LangChain and LLMs. I am confident that I will be able to help you by developing AI-based plant disease detection and recommendation system as per your requirements. Similar work done in the past: - Fetal brain abnormality detection - Multiclass intent classification - AI Powered Copilot for Text2SQL Query - Recommendation system for eLeraning contents - Semantic search engine - Topic modeling Relevant Skills: - Python - Django/Flask/React.js - Agentic AI - NLP - GPT4o/Gemini/Llama3.2 - AWS/VPS - LangChain - MySQL - TensorFlow/PyTorch - Google Colab - OpenCV Let's have a chat to understand the project objective and the dataset in details. I assure you to deliver the best quality results and ensure the customer satisfaction. Looking forward to hearing from you soon. Thanks for the opportunity.

@nrajuu07

I'm a computer vision engineer and full-stack developer experienced in agricultural AI applications. I'll build a mobile-friendly web app where farmers upload a leaf photo and instantly receive disease name, description, causes, treatment, prevention tips, and fertilizer/pesticide recommendations — powered by a trained CNN model in PyTorch/TensorFlow. Deliverables include the trained model with documented metrics, responsive frontend, Python backend, Dockerfile for one-click Ubuntu deployment, and a concise user guide and API reference. Full source code ownership transferred at handoff. Ready to start immediately.

@chiragardeshna

Hi there, Strong alignment with this project comes from experience building AI-powered computer vision systems with deep learning models, image classification workflows, and responsive web applications for agriculture and healthcare domains. Clear understanding of the requirement to develop a web-based plant disease detection platform where farmers can upload crop images and instantly receive disease analysis, treatment guidance, prevention tips, and fertilizer or pesticide recommendations. Hands-on expertise with Python, TensorFlow/PyTorch, CNN-based image classification, Flask/FastAPI backends, Docker deployment, responsive frontend development, and AI model optimization ensures accurate and scalable system performance. Risk is minimized through structured dataset preparation, model validation with unseen image testing, lightweight inference optimization for rural connectivity, reproducible training pipelines, and clear deployment documentation for Ubuntu VPS hosting. Will the initial AI model focus on a specific crop category first, or should the MVP support multiple regional crops from the beginning? Available to start immediately happy to share a quick demo or discuss next steps. Recent work: https://www.freelancer.com/u/chiragardeshna Regards Chirag

@Web1devloper

As an experienced Data Analyst and Python developer with more than 8 years in the field, I'm confident in my ability to deliver what you need for your AI-Based Plant Disease Detection and Recommendation System project. My profound understanding of Machine Learning (ML) and its libraries including TensorFlow, PyTorch, Scikit-learn would ensure that I’ll build a robust convolutional-network model for accurate disease diagnosis. Not only will I design a clean, user-friendly web interface for easy image upload and results display but also provide all the necessary back-end code and documentation to reproduce and deploy the model. My extensive expertise in data storytelling and dashboard development using tools like Power BI, Looker, Tableau will surely contribute to delivering a responsive frontend solution that meets your expected standard. What sets me apart from other bidders is not just my technical proficiency but my ability to leverage data to drive actionable insights; significantly essential for interweaving technology into the agricultural domains. Using my skills in statistical analysis, hypothesis testing, A/B testing, I'll ensure to create an AI infrastructure that is spot-on for common regional crops with a mobile-friendly interface that works seamlessly even on rural internet connections. It's time to equip farmers with an efficient crop-care system; I'm ready to make this happen for you!

@manoval09

Hi, Getting farmers a fast, accurate disease diagnosis from a single leaf photo — without requiring agronomic expertise on their end — is exactly the kind of focused problem I've solved with transfer learning on plant datasets. For this project I'd use a fine-tuned EfficientNet-B0 (pretrained on PlantVillage, which covers 38 disease classes across 14 crops) served via a Flask REST API. The frontend gets a drag-and-drop upload component with client-side JPEG/PNG validation, and the model returns a disease label, confidence score, and treatment recommendation pulled from a lightweight PostgreSQL lookup table. This approach avoids custom model training from scratch — keeping it inside your budget — while delivering solid accuracy (typically 90%+ on common leaf diseases). I'd containerize with Docker so deployment is straightforward wherever you're hosting. Within 24 hours of kickoff I can have the model integrated into a working API endpoint with a basic UI so you can see a real prediction against an actual leaf photo — that gives you a concrete checkpoint before we go further. One question worth clarifying now: does the recommendation output need to be multilingual, or is English sufficient for your farmers? Best regards, Val

@Visharaa2024

Hello, I’m highly interested in developing your AI-powered crop disease detection platform and can deliver a reliable, production-ready solution designed for practical agricultural use. I have experience building computer vision applications using TensorFlow, CNN architectures, and responsive web technologies to create fast, accurate, and mobile-friendly AI systems. I can develop a lightweight web platform where farmers upload crop or leaf images and instantly receive disease identification, symptoms, causes, treatment recommendations, prevention methods, fertiliser or pesticide suggestions, and additional crop-care guidance directly on the same screen. The system will be optimised for rural internet conditions while maintaining strong prediction accuracy and fast response times. I can train and fine-tune the AI model using open agricultural datasets or your custom dataset to improve detection performance across regional crops. Along with the trained model, I will provide complete backend and frontend source code, reproducible training scripts, Docker deployment setup for Ubuntu VPS hosting, API documentation, performance metrics, and a concise user guide for easy deployment and maintenance. My focus will be on delivering a scalable, accurate, and user-friendly solution that farmers can confidently rely on in the field while ensuring full source code ownership transfer upon successful project completion.

@adityabhalsod007

Hi, Your project combines AI, computer vision, and practical agriculture, which makes it particularly interesting. I understand that the goal is not just disease classification, but providing farmers with immediate, actionable guidance after uploading a crop image. I can build a complete end-to-end solution using Python and modern deep learning frameworks such as TensorFlow or PyTorch. The system will allow users to upload a leaf or crop image, run inference through a trained CNN-based model, and instantly display the disease diagnosis along with treatment recommendations, likely causes, prevention methods, fertilizer/pesticide suggestions, and crop-care guidance. Deliverables will include: • Trained and documented AI model with evaluation metrics • Responsive web application optimized for mobile devices • Fast image upload and real-time prediction workflow • Complete backend source code and training pipeline • Dockerized deployment for Ubuntu VPS • API documentation and user guide • Full source code ownership transfer My approach focuses on both model accuracy and usability. I can leverage proven transfer-learning architectures (EfficientNet, ResNet, MobileNet, etc.) and optimize inference speed to ensure good performance even on slower rural internet connections. I would be happy to review your crop types, available datasets, and accuracy expectations to design the most effective solution. Best regards, Aditya

@leewayde

I HAVE DONE SOMETHING SIMILAR BEFORE! Your need for a clean, professional, user-friendly, and seamless web solution that integrates AI for on-the-spot crop disease diagnosis perfectly aligns with my experience. Ensuring the system is fast and mobile-friendly to operate reliably over rural internet is key, along with automated, accurate analysis displayed immediately on the same page. I specialize in developing AI-powered web applications using Python, TensorFlow, and Docker for smooth deployment. My expertise includes building convolutional networks tailored for image classification and creating responsive front-ends optimized for low bandwidth scenarios. You won’t find someone more aligned with what you’re looking for. Come chat with me, worst case you get a free consultation :) Regards, Lee-Wayde

@ViktorUmanskyi

I believe my robust experience in scalable web development makes me an ideal candidate for creating your AI-based plant disease detection and recommendation system. Having worked predominantly with Django/DRF and Python, I am adept at delivering powerful solutions that align perfectly with your outlined requirements. My work ensures reliability; which means clean code, predictable output and stable performance - even under load, so it would be a perfect addition to your project as it needs to process images even under rural internet connections. Moreover, my proficiency with deep learning frameworks like TensorFlow and PyTorch offer the necessary competencies in training the convolutional-network based AI model that you desire. I've successfully built and maintained SaaS backends, integrated payment systems such as Stripe, as well as collaborated effectively with frontend teams to ensure a smooth release process. Lastly, I understand how crucial it is to guaranteeting ownership of source codes post-handoff. You have my word that your project's source code will be transferred completely to you. As evidence of this, I invite you to place trust in me by testing the system with an unseen image set before accepting completion. It would be an honor to collaborate with you on such a potentially impactful project for farmers and the agricultural industry at large. Let's discuss further details!

@hugog17

⭐⭐⭐⭐⭐ Senior Computer Vision & AI Application Developer ⭐⭐⭐⭐⭐ Hello there, I am a machine learning engineer specialising in plant disease detection systems using convolutional neural networks and lightweight web deployment for low-bandwidth environments. A plant disease model that works reliably in the field needs to handle real-world image quality — photos taken in varying light, at odd angles, with partial leaf coverage — not just clean dataset images. Model accuracy on curated test sets rarely matches field performance without augmentation training that mimics those conditions. Built CNN-based crop disease detection systems using PyTorch with EfficientNet backbone, achieving 94% validation accuracy on PlantVillage dataset across 38 disease classes. FastAPI backend handled image inference in under 800ms, and the responsive frontend was optimised for slow rural connections with compressed assets and minimal JavaScript overhead. Docker deployment with a one-click setup script means the system runs consistently across different Ubuntu VPS environments without dependency conflicts. Are the regional crops you're targeting covered by PlantVillage or a similar open dataset, or will the custom dataset you supply cover disease classes not available publicly?

@alimohamed009

With my background as a Data Scientist and Machine Learning Engineer, I can build a complete end-to-end AI-powered crop disease detection system that is accurate, fast, and production-ready. I specialize in CNN-based computer vision models (TensorFlow/PyTorch) and deploying them using Python backends like Flask or FastAPI, along with Docker-based deployment on Linux servers. I have experience handling full ML pipelines including data preprocessing, model training, evaluation, and web integration. For your project, I will develop a system where users upload a leaf image and instantly receive disease prediction along with clear insights such as description, causes, treatment, prevention tips, and recommendations. The solution will be optimized for speed, accuracy, and mobile-friendly usage in real field conditions. What I will deliver: • Trained CNN model with evaluation metrics • Clean web interface for image upload and instant results • Python backend (Flask/FastAPI) for inference • Full ML pipeline (training + preprocessing + deployment code) • Docker setup for easy VPS deployment • Well-documented source code with usage guide • Full ownership transfer after delivery I can start immediately and deliver an initial working version within 7–10 days depending on dataset size and complexity.

@Muaadh11

Bid: I’ve built production YOLO-based CV pipelines at SpectrifyAI — tea leaf grading, defect detection, 2,000+ image datasets — so crop disease detection is directly in my wheelhouse. I’ll fine-tune a YOLOv8/EfficientNet model on PlantVillage + your custom images, hitting reliable mAP on regional crops. The stack: FastAPI backend, PyTorch inference, clean mobile-first upload UI optimised for low bandwidth. You get a Dockerfile for one-click Ubuntu VPS deploy, training scripts with full metric docs, and a straightforward API reference. Results — disease name, causes, treatment, fertiliser/pesticide recommendations — display instantly on-page, no agronomist needed. Source code transfers fully at handoff, and I’m happy to benchmark against your unseen test set before acceptance. Let’s talk scope and dataset.

@adhamamrs

Hello, Your project caught my attention because it combines Computer Vision and Deep Learning with a real agricultural use case that can help farmers make faster decisions in the field. I’m Adham Morgan, a senior Computer Science student specializing in AI and currently completing the DEPI – Microsoft Machine Learning Engineer Track. I have experience building ML workflows, preprocessing pipelines, and AI-based analysis systems using Python. I can develop a complete end-to-end solution including: • Plant disease detection model using TensorFlow or PyTorch • Image preprocessing and augmentation • Real-time disease prediction from uploaded images • Responsive web interface optimized for mobile devices • Backend API and Docker-ready deployment setup • Clean, documented source code The system will display: Disease name, causes, treatment methods, prevention tips, and crop-care recommendations. Tech Stack: Python, TensorFlow/PyTorch, OpenCV, Flask/FastAPI, HTML/CSS/JS, Docker. Best regards, Adham Morgan

@zaina957

Experienced AI/ML Engineer with proven ML projects. I can deliver this accurately and on time. Let’s talk!

@warrick1718

This aligns perfectly with my skill set. Your need for a clean, professional, user-friendly, and seamless web-based system that integrates an AI model for immediate plant disease diagnosis is clear. I understand the importance of a fast, mobile-friendly interface capable of handling uploads and delivering automated, on-screen feedback without delays, even on rural internet connections. I offer expertise in Python development using TensorFlow and PyTorch to build robust, accurate convolutional networks. While I am new to freelancer, I have tons of experience and have done other projects off site. I can also provide well-documented code, deployment scripts, and user guides as requested. I would love to chat more about your project! Regards, Warrick Van Eeden

@himanshu0818

Hi there, This is a very impactful project and I’d love to help you build it. I have 15+ years of experience in web and AI-based development, and I also use AI in my workflow so I can deliver faster, reduce cost, and maintain high-quality results. I can build: • Accurate crop disease detection model (TensorFlow/PyTorch) • Clean, mobile-friendly web interface for instant image upload & results • Fast backend with real-time prediction (optimized for low internet speed) • Complete system with Docker setup for easy deployment on VPS • Clear documentation for training, API, and usage I’ll ensure the model is reliable, well-tested, and easy to scale in the future. I can start immediately and deliver a complete working solution. Let's chat, Himanshu

@JeganAi

Hello, I am Jalastine Jegan I am interested in building your crop disease detection web application. I have experience in Python, Machine Learning, Computer Vision, and web development. I understand the problem farmers face in getting quick disease identification and treatment advice. I can build a simple and reliable system where users upload a JPG or PNG leaf image, and the AI model instantly shows: • Disease name • Short description • Causes • Treatment and prevention tips • Recommended fertilizers or pesticides • Extra crop care advice I will develop the backend using Python with TensorFlow or PyTorch and create a fast, mobile-friendly website optimized for rural internet connections. Deliverables: • Trained AI model with performance metrics • Responsive web application • Full source code • Docker/setup files for Ubuntu VPS • User guide and API documentation The system will be clean, fast, and easy to use. Full source code ownership will be transferred after project completion. Thank you.

@ZaidALayoubi

I have experience building CNN models in PyTorch for classifying medical data with accuracy. I can also build a plant disease diagnosis model with a FastAPI backend.

@bilal6901

Hi there, I understand that you need a responsive, web-based AI solution to help farmers diagnose crop diseases instantly from leaf images. Using Python with TensorFlow or PyTorch, I can develop a robust convolutional neural network that identifies common crop diseases with high accuracy. The system will provide on-screen results including disease name, description, causes, treatment, prevention tips, and suggested fertilizers or pesticides, all in a clean, mobile-friendly interface optimized for low-bandwidth connections. My approach is to train the model on open datasets and your curated images, ensuring reliable predictions across regional crops. I will build a fast back-end to handle image uploads and inference in real-time, paired with a lightweight, responsive front-end that displays results immediately. Clear documentation, reproducible training scripts, and deployment-ready code (including a Dockerfile for Ubuntu VPS) will make setup and maintenance straightforward. With attention to accuracy, speed, and usability, I will deliver a fully functional system that empowers farmers to make informed decisions in the field without waiting for external guidance. All source code and documentation will be clean, modular, and ready for hand-off. Regards, Bilal