• Naziya Sande
• Purva Chaudhari
• Ritesh Sonawane
• Shreyas Sonawane
• prof.Jagruti Wagh

Crop diseases are significant problems to agricultural productivity and farmer livelihoods, requiring early and accurate diagnosis for effective management. Early and accurate detection of crop diseases is essential for effective treatment and minimizing losses.Traditional methods often face challenges in early detection and accurate disease classification.Deep learning, a subset of AI, has demonstrated significant potential in agricultural image analysis, providing enhanced accuracy for crop disease detection and prediction. This review explores deep-learning models applied to crop disease detection, focusing on convolutional neural networks, recurrent neural networks, and ensemble methods. In addition to automated detection, the system integrates a dedicated portal for agricultural experts to provide farmers with personalized advice and actionable recommendations. Timely notifications and expert suggestions are delivered via farmer’s preferred communication channels, enhancing disease management, crop health and decision-making.

Crop disease detection, Deep learning, image processing, Convolutional Neural Networks, expert recommendations, real-time monitoring.

"Smart Fertilization Advisor For Farmers Using Deep Learning And Expert Insights", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.k755-k758, April-2025, Available :http://www.jetir.org/papers/JETIR2504A93.pdf

"Smart Fertilization Advisor For Farmers Using Deep Learning And Expert Insights", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppk755-k758, April-2025, Available at : http://www.jetir.org/papers/JETIR2504A93.pdf