This repository contains a Deep Learning project based on Convolution Neural Network or CNN for short. In this project model is trained to identify the crop and whether the crop is infected or not ...

Feb 19, 2025 · This project leverages deep learning to identify crop diseases from images. Built with TensorFlow, the app allows users to upload crop images and predicts the disease. It features a user-friendly interface, feedback submission, and an informative "About" section. Perfect for early disease detection in crops.

A comprehensive project utilizing CNN and Deep Learning to detect and classify diseases in plants, enabling farmers and experts to prevent outbreaks and protect crop yield.

Dec 1, 2023 · Proposed a deep learning-based model for crop disease detection. Provides a higher accuracy rate of 98.98% using ResNet-50 for disease detection. Ensure farmers save resources and prevent economic loss. Agriculture plays a significant role in every nation's economy by producing crops.

Plant disease detection is crucial for ensuring food security and agricultural sustainability by enabling timely intervention to prevent crop losses. Recent advancements in machine learning and image processing have transformed plant pathology, offering …

Feb 11, 2025 · This paper presents a deep learning-based solution for the detection and classification of crop diseases in tomato, banana, and mango leaves using convolutional neural networks (CNNs). As crop diseases significantly impact agricultural productivity, the need for early, accurate detection is crucial for ensuring food security and economic stability. The proposed model leverages large datasets ...

This review paper provides a comprehensive overview of crop disease detection using machine learning techniques. We explore various datasets, preprocessing methods, machine learning algorithms, deep learning models, performance metrics, real-world applications, and challenges in deploying such systems. Furthermore, we discuss current trends and ...

Apr 2, 2025 · Plant leaf diseases significantly threaten agricultural productivity and global food security, emphasizing the importance of early and accurate detection and effective crop health management. Current deep learning models, often used for plant disease classification, have limitations in capturing intricate features such as texture, shape, and color of plant leaves. Furthermore, many of these ...

Here we have used various Deep Learning classifiers to develop crop recommendation systems and have compared their performance matrices, using Deep Learning models LSTM, RNN, DENSE N/W, and ANN. We have also proposed an ensemble model, which is a combination of all the DL models. The proposed ensemble model outperformed all the DL classifiers ...

Mar 1, 2019 · To assist the identification and detection of different crops that characterize the agricultural missions, we introduce a novel domain-specific dataset named CropDeep, which consists of vegetables and fruits that are closely associated with PA.