Imagine walking through a lush guava orchard, the air filled with the sweet aroma of ripe fruit. For farmers, this idyllic scene is more than just a picture-perfect moment, it’s their livelihood. But beneath the surface, a silent threat looms: diseases like Anthracnose and Fruit Flies can turn this paradise into a nightmare, destroying crops and livelihoods in a matter of weeks. What if we could stop these diseases in their tracks? What if we could give farmers the tools to detect and combat these threats before they spiral out of control? This is not just a dream, it’s a reality Global Business is building. Welcome to the story of how deep learning is revolutionizing guava farming.
The Challenge: A Farmer’s Nightmare
Guava is a beloved fruit, cherished for its flavor and nutritional value. But it’s also highly vulnerable to diseases like:
- Anthracnose, a fungal infection, can cause dark, sunken lesions on the fruit, rendering it unsellable.
- Fruit Flies, on the other hand, lay eggs inside the fruit, leading to infestation and decay. For farmers, these diseases mean lost income, wasted effort, and sleepless nights.
Traditionally, detecting these diseases has been a manual process, farmers inspect each fruit, hoping to catch signs of trouble early. But this method is far from perfect. It’s time-consuming, labor-intensive, and often inaccurate. By the time a disease is spotted, it’s often too late. This is where technology steps in.
The Solution: Deep Learning to the Rescue
At Global Business, we asked ourselves: What if we could automate disease detection? What if we could give farmers a tool that’s faster, more accurate, and more reliable than the human eye?
The answer lay in Convolutional Neural Networks (CNNs), a type of deep learning model designed to analyze visual data. We set out to build a system that could classify guava fruits into three categories: Anthracnose, Fruit Flies, and Healthy Fruits.
Here’s how we did it.
The Journey: From Data to Diagnosis
Step 1: Building the Dataset
Every great AI model starts with great data. We collected 473 images of guava fruits from orchards in Rajshahi and Pabna, Bangladesh, regions known for their guava production. These images were carefully labeled by plant pathologists to ensure accuracy.
But we didn’t stop there. Using techniques like unsharp masking and CLAHE (Contrast Limited Adaptive Histogram Equalization), we enhanced the images to bring out subtle details. We also used data augmentation to expand the dataset to 3,784 images, ensuring our models had plenty of data to learn from.
Step 2: Choosing the Right Models
We implemented two state-of-the-art deep learning models for this project:
● MobileNetV2: A lightweight, efficient model perfect for real-world applications.
● ResNet: A powerful model known for its accuracy in image classification tasks.
Both models were trained on our dataset, and the results were nothing short of remarkable.
Step 3: Training and Testing
After weeks of training, our models were ready to put to the test. The results?
● MobileNetV2 achieved an accuracy of 98.56%.
● ResNet outperformed even our expectations with an accuracy of 99.96%.
But numbers only tell part of the story. Let’s see the models in action.
The Results: Seeing is Believing
Here are some examples of our models’ predictions of real-world scenarios:
At Global Business, we are committed to pushing the boundaries of agricultural AI. Our future plans include:📲 A Mobile App – Allowing farmers to scan and analyze guavas instantly with their smartphones.Broadening Crop Coverage – Expanding our AI model to detect diseases in other fruits like mangoes, apples, and citrus crops. On-Field Edge AI – Developing real-time, offline disease detection for remote farming areas.
With AI-driven farming solutions, we are redefining agriculture’s future—one guava at a time. 🍃
If you’re a farmer, agribusiness leader, or researcher looking to integrate AI into your workflow, let’s connect!
📩 Contact us at [Your Contact Info] to learn more.
🌟 Let AI protect your crops, secure your future, and transform farming!