Empowering Pineapple Farmers through Drone Imagery Analysis

Christine Straub

Backend Engineer
ML Engineer
AI Developer

Role: ML Engineer | AI Developer

Project Objective:

The primary objective of this project was to develop a computer vision system that utilizes drone imagery to generate valuable information and insights for pineapple farmers in Africa. By providing accurate statistics and maps, the system aims to help farmers make informed decisions, optimize their farming practices, and ultimately increase their yield and livelihood.

Key Achievements:

  1. Flowering Plant Identification:
    • Developed a system that accurately identifies and localizes pineapple plants that have not flowered.
    • This information is crucial for farmers, as it enables them to induce flowering in non-flowering plants using chemical or hormonal solutions, ensuring optimal fruit production.
  2. Fruit Counting:
    • Implemented a feature that counts the number of pineapple fruits in the drone imagery.
    • By knowing the expected production, farmers can make necessary investments and plan their resources effectively.
  3. User-Centric Design:
    • Tailored the system to cater to the needs of various user types, including farmers, drone operators, and extension agents.
    • Ensured that the information generated is easily accessible and understandable for all stakeholders.
  4. Image Preprocessing:
    • Developed advanced preprocessing techniques to enhance the quality of drone images before applying machine learning algorithms.
    • Implemented noise removal, sliding window resolution standardization, and geometric/color calibration to ensure accurate and reliable results.
  5. Multi-Label Object Detection:
    • Developed a custom Convolutional Neural Network (CNN) model for multi-label object detection.
    • Trained the model using a custom dataset, fine-tuned it for optimal performance, and deployed it for real-time analysis.
    • The model effectively identifies and classifies pineapple fruits, flowers, and plants without flowers.
  6. Efficient Handling of Large Images:
    • Developed a robust algorithm to process and analyze large-sized drone images efficiently.
    • Implemented techniques such as object detection, object classification, sliding window, and feature prediction to extract meaningful information from the imagery.

Technical Stack:

The project leveraged a range of computer vision and machine learning techniques, including object detection, object classification, sliding window, image processing, geometric calibration, and feature prediction.

The system was built using popular libraries such as OpenCV, TensorFlow, PIL, NumPy, CUDA, cuDNN, and GeoTIFF.

Training Data:

The dataset used for training and testing the model was carefully curated and split into two subgroups: 70% for training and 30% for testing.

The model was trained using the faster-rcnn-resnet101 architecture, with a total of 3,186 images and 3 label classes.

The training process involved 160,000 epochs to ensure optimal performance.


By combining cutting-edge computer vision techniques with drone technology, this project demonstrates the potential to revolutionize pineapple farming in Africa. The developed system empowers farmers with actionable insights, enabling them to make data-driven decisions and optimize their farming practices for increased productivity and profitability.

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