• Lavanya S
• Jeevitha N
• Kruthika S
• Sahana M G
• Ranjana Thakuria

This study investigates the use of Convolutional Neural Networks (CNNs) for plant disease detection, a critical aspect of ensuring global food security. Through the analysis of recent advancements, the paper outlines the methodology involving dataset collection, preprocessing, and CNN model training. Various CNN architectures and transfer learning techniques are explored for improved classification performance. Strategies such as data augmentation are discussed to enhance model robustness. Experimental results demonstrate the efficacy of CNN-based approaches in achieving high accuracy and reliability in disease detection across diverse crop species. Despite challenges like dataset imbalance and overfitting, CNNs show significant promise in revolutionizing agriculture by enabling timely disease diagnosis, thereby bolstering crop productivity and sustainability.

Convolutional Neural Network, Dataset.

"Plant Disease Detection Using Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.e202-e205, May-2024, Available :http://www.jetir.org/papers/JETIR2405424.pdf

"Plant Disease Detection Using Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppe202-e205, May-2024, Available at : http://www.jetir.org/papers/JETIR2405424.pdf