Gauri Kalnoor

Plant diseases remain a significant threat to global agriculture, necessitating rapid and

accurate detection to minimize crop loss. This paper presents a lightweight, end-to-end system for plant

leaf disease detection and severity estimation, optimized for real-time field deployment. We propose a

custom Convolutional Neural Network (CNN), built using PyTorch, trained on the PlantVillage dataset

to classify leaves as healthy or diseased with a test accuracy of 92.06%. To enhance its practical relevance,

With the increase in world population, agricultural planning is significant to ensure food security. Timely recommendations for crops could be valuable for planning food production and maintaining food sustainability. This proposed work suggests a crop recommendation model considering physical soil characteristics, chemical soil characteristics, climate, and crop characteristics, using Improved Deep Belief Networks (IDBN). For this study, four important Indian crops—rice, maize, finger millet and sugarcane were taken into account.