🔗 Git Hub
🔗 Report
Description
In the ever-evolving landscape of agriculture, the incorporation of cutting-edge technologies has become indispensable for enhancing crop yield, minimizing economic losses, and ensuring food security. One of the most pressing challenges that farmers face annually is the detrimental impact of diseases on crops, particularly in the case of potato, tomato, and pepper plants. Crop diseases not only lead to significant financial setbacks for farmers but also contribute to food scarcity, posing a considerable threat to global food production.
To address this critical issue, this project embarks on the first leg of an end-to-end deep learning project within the agriculture domain. The primary objective is to leverage the power of deep learning and image classification techniques to create a supervised learning model. This model will revolutionize the way farmers identify and manage diseases in their crops, ultimately bolstering crop yield and reducing economic losses.
Farmers encounter substantial economic losses and crop wastage each year due to the presence of various diseases in potato, tomato, and pepper plants. This project aims to develop an image classification system employing supervised learning methods. By training this model, we intend to empower farmers with a userfriendly application capable of swiftly diagnosing the health status of their plants. Through a simple picture capture and analysis process, the application will provide valuable insights into whether a plant is afflicted by a disease or is healthy.
This endeavor holds the promise of transforming the agriculture sector by enabling timely disease detection, thereby facilitating prompt intervention measures. As we delve into the details of this deep learning assignment, we will explore the methodologies, datasets, algorithms, and technologies employed in developing this groundbreaking solution for the agricultural community. The ultimate goal is to equip farmers with a powerful tool that will enable them to make informed decisions, mitigate crop diseases, and maximize their agricultural output.
To determine the most effective and accurate model for plant disease diagnosis in our agriculture domain project, we will adopt a comprehensive approach. Our strategy involves training the dataset separately using four distinct supervised learning models, such as Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), and Decision Trees. The primary objective is to identify which of these models yields the highest accuracy and reliability in classifying plant health.