Author(s) :   Dr. K. Pandikumar¹, Naveen Kumar P², Thilocigan K³, Sanjay A⁴, Rishidharan V⁵

Abstract : Agriculture plays a crucial role in global food security, and advancements in machine learning are significantly improving the way we manage agricultural resources. This project presents a deep learning-based framework using Convolutional Neural Networks (CNNs) to analyze and classify plant health, freshness, and nutritional content in crops such as wheat, rice, fruits, and vegetables. By leveraging large datasets of crop images, the model detects plant diseases, assesses freshness, and estimates nutritional content based on visual cues. The system provides real-time insights for farmers, agronomists, and food industry professionals, facilitating early disease detection, quality control, and sustainable farming practices. The proposed CNN model is trained using a variety of agricultural images, pre-processed to normalize environmental factors and enhance disease or quality features. The results demonstrate high accuracy in detecting diseases such as rust, blight, and mold, as well as the ability to estimate freshness levels and key nutritional elements such as vitamins and minerals. This research aims to provide a cost-effective and scalable solution for enhancing agricultural practices and ensuring food quality.

Keywords: Convolutional Neural Networks (CNN), Agricultural Analysis, Plant Diseases, Freshness Detection, Nutritional Content, Wheat, Rice, Fruits, Vegetables, Deep Learning, Crop Health, Disease Classification, Quality Control.

DOI: Available on author(s) request

Pages : 70-78

*Authors are invited to submit papers through E-mail at editor.ijarcsms@gmail.com