• T.BALAPRABHA

To effectively manage tomato crops, illnesses must be detected early and accurately, as they can have a substantial impact on output and quality. This study investigates the use of deep convolutional neural networks (CNNs) to identify tomato leaf illnesses using image processing techniques. We present a unique approach that uses a CNN architecture to analyze leaf pictures, recognizing and classifying various illness symptoms with excellent accuracy. Our methodology entails gathering a large dataset of tomato leaf photos, preprocessing them to improve feature visibility, and then training a CNN model on the data. The network's performance is measured using measures including accuracy, precision, recall, and F1 score. The results show that the CNN model has great accuracy in disease diagnosis, highlighting its promise as a robust tool for automated agricultural.

illnesses of tomato leaves, Processing of images, deep Convolutional Neural Networks (CNNs),Diagnosing illness, Automated Diagnosis in Agricultural Monitoring, Plant Pathology, Computer Vision, Disease Classification, Machine Learning Accuracy Analysis of Agricultural Leaf Images, Extraction of Features, Model Assessment and Dataset Preparation.

"Detecting tomato leaf diseases by image processing through deep convolutional neural networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 9, page no.d231-d238, September-2024, Available :http://www.jetir.org/papers/JETIR2409328.pdf

"Detecting tomato leaf diseases by image processing through deep convolutional neural networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 9, page no. ppd231-d238, September-2024, Available at : http://www.jetir.org/papers/JETIR2409328.pdf