Simranpreet Kaur

AI-driven image recognition significantly enhances the early detection of crop diseases and pest infestations by analyzing images of plants and fields to identify subtle symptoms and patterns that might be missed by the human eye. This allows for timely intervention, minimizing crop losses and reducing the need for broad-spectrum chemical treatments.

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AI-driven image recognition is transforming early detection of crop diseases by identifying subtle visual symptoms—such as lesions, color shifts, or texture anomalies—that often precede visible plant distress. These systems use convolutional neural networks (CNNs) and transfer learning to process high-resolution images captured from leaf surfaces, enabling farmers to act before infestations spread.

In my research ("AI-based Crop Pest Detection Using Deep Image Recognition: A Leaf Pattern Analysis Approach"), we developed a system that analyzes morphological and spectral features from infected and healthy crops. The model was trained on region-specific pest datasets and achieved over 92% accuracy in classifying infestation stages. This work supports targeted treatment strategies, minimizing pesticide overuse and boosting yield predictability.

Such AI tools, when deployed via drones or edge devices, offer real-time, scalable surveillance for smart agriculture. The integration of image recognition with IoT and precision farming can shift intervention from reactive to proactive—vital for food security in climate-stressed regions.

AI-based Crop Pest Detection Using Deep Image Recognition: A Leaf Pattern Analysis Approach :-

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