AI Technology Enhances Disease Detection in Pakistan’s Sugarcane Fields

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• Machine learning techniques, such as Support Vector Machines (SVM-RBF), have been employed to detect asymptomatic diseases like Ratoon Stunting Disease (RSD) in sugarcane. These methods utilize freely available satellite-based multispectral imaging, achieving classification accuracies between 85.64% and 96.55%, depending on the sugarcane variety.
• The development of optimized ensemble models, such as SugarcaneNet2024, has significantly improved disease classification accuracy. By combining LASSO-regularized pre-trained models like InceptionV3 and ResNet152V2, these models have achieved outstanding performance metrics, including 99.67% accuracy and 100% precision and recall, enabling precise identification of sugarcane diseases.
• The use of unmanned aerial vehicles (UAVs) equipped with multispectral cameras allows for the collection of high-resolution images of sugarcane fields. AI algorithms process these images to detect diseases such as White Leaf Disease, facilitating early intervention and targeted treatment strategies.
• AI-driven disease detection systems contribute to precision agriculture by providing real-time monitoring of crop health. This enables farmers to implement targeted disease management strategies, optimize pesticide use, and enhance overall crop productivity, aligning with sustainable farming practices.

Pakistan’s sugarcane industry is receiving a technological boost as artificial intelligence (AI) is being deployed to detect crop diseases more efficiently. 

This innovation is set to help farmers improve yields and reduce losses caused by plant infections, particularly in Punjab and Sindh, the country’s major sugarcane-producing regions.

Led by researchers at the National University of Sciences and Technology (NUST) in Islamabad, the project uses AI-powered image recognition systems to identify early signs of disease in sugarcane leaves. 

Farmers can simply upload images of their crops via a mobile application, which then analyses the visuals and diagnoses potential diseases such as red rot and smut.

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The system not only detects diseases with high accuracy but also provides farmers with timely advice on treatment options and preventive measures. By reducing the time between disease onset and action, the technology is expected to save farmers significant losses and reduce reliance on chemical sprays.

Dr. Muhammad Junaid Khan, the lead researcher at NUST, noted that many farmers lack access to expert agronomists, particularly in rural areas. “AI bridges that gap. It empowers farmers with instant information that used to take days or even weeks to access,” he explained.

The project is part of a broader initiative to integrate digital technology into Pakistan’s agriculture sector. With sugarcane being a key cash crop, accounting for a large share of rural livelihoods, the success of such AI tools could influence broader adoption across other crops.

Government officials have expressed interest in expanding the project nationally, citing its potential to enhance food security, cut production losses, and boost the digital transformation of agriculture in Pakistan.

This marks a significant step forward in bringing precision agriculture to the forefront of farming practices in the region.