CropPulse: AI Sentinels againts Crop Diseases

• Suyash Srivastava

  GD Goenka University

  Author

• Yashna

  GD Goenka University

  Author

Agriculture is crucial for global economy, particularly in developing countries where the majority populace relies on it as a source of livelihood. One of the greatest challenges farmers are confronted with today is the timely and precise detection of crop diseases, which affect crop yield and quality. Traditional disease identification has relied on manual inspection, which could be laborious, faulty, and largely out of reach for smallholder farmers due to limited resources or unavailability of expert data. In a bid to overcome such a handicap, the current study aims at developing a detection system for crops through image processing in combination with machine learning, that is, the (SVM) classifier. This paper explains a variety of methods, with reference to recent upgrades in machine & deep learning, digital image processing that made disease diagnosis based on leaf texture analysis more precise and reliable. The paper suggests a reliable system allowing early and efficient plant disease diagnosis using quality imaging equipment, sophisticated data analysis, and smart classification algorithms.
Experiments shows that the system can accurately identify a number of plant diseases. The findings demonstrate the potential of using automation to enhance productivity significantly, while also improving sustainability in agriculture. These experimentally demonstrate high accuracy in detecting multiple plant diseases, emphasizing the potential

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