Pulkit Sinha
Jayadip Sahoo
Abhikalp Srivastava
Ayush Jagatdeb
Agriculture is the backbone of the world's economy, providing food and raw materials for various industries. However, farmers face numerous challenges in crop cultivation, ranging from price uncertainty to crop diseases and a lack of information on which crops to grow. These challenges not only affect farmers' livelihoods but also impact food security and the global economy.
How can farmers improve their crop yields while reducing their costs and risks associated with farming, and how can they stay up-to-date with the latest news and innovations in the agricultural industry?
Farmers across the world face significant challenges in crop cultivation, including weather patterns, soil quality, access to resources, and price uncertainty. These challenges have far-reaching economic and social implications, as agriculture is a major source of employment and income. Detecting and diagnosing crop diseases is often difficult, and farmers lack access to information on which crops to grow and how to optimize their cultivation. Innovative solutions that leverage technology and data can help to address these challenges, and platforms like CropStop can empower farmers with real-time market data, disease detection tools, and crop recommendations based on machine learning models to build a more resilient and sustainable agriculture sector.
Welcome to CropStop, a one-stop solution for all your agricultural needs. Our platform is designed to provide farmers with the tools and information they need to succeed in today's rapidly changing agricultural landscape.
News and Latest Innovations: Stay up-to-date with the latest news and innovations in the agricultural industry with CropStop. Our platform uses the Newscatcher API to provide users with real-time news and insights from trusted sources around the world. From new farming technologies to market trends and policy developments, our news service covers it all.
At CropStop, we offer a comprehensive suite of services designed to help farmers improve their crop yields and reduce the risks and costs associated with farming. Our services include:
Predict the prices of crops to help farmers plan and optimize their production.
Use machine learning models to detect and diagnose crop diseases, enabling farmers to take proactive steps to manage disease outbreaks and protect their crops.
Get personalized crop recommendations based on machine learning models that take into account local weather patterns, soil conditions, and other factors that affect crop growth.
We are committed to providing the best possible support and service to our users. If you have any questions or feedback, please don't hesitate to contact us using the contact form on our website. Our team is always available to help you with any questions or concerns you may have.
* Libraries: numpy, pandas, matplotlib, tensorflow and keras
* Methodology: - importing dataset using keras preprocessing - passing images to function to resize - dataset partitioning - data augmentation - using CNN model - creating various CNN layers - using adam compiler - training the model - accuracy - prediction - save model using pickle dump
* Libraries: numpy, pandas, matplotlib, scikit-learn
* Methodology: - import dataset - preprocessing dataset - checking missing values - drop unnecessary columns - one hot encoding - creating dummies - training model - randomForestClassifier - accuracy - confusion matrix - prediction - save using pickle dump
* Libraries: numpy, pandas, matplotlib, scikit-learn
* Methodology: - import dataset - preprocessing dataset - checking missing values - drop unnecessary columns - one hot encoding - creating dummies - training model - randomForestClassifier - accuracy - confusion matrix - prediction
The potato disease detector is
a machine learning model that uses a deep learning algorithm to detect
diseases in potato plants by analyzing images of their leaves. It
identifies symptoms such as discoloration or unusual growth patterns,
and continually updates and refines its accuracy with more images. This
tool helps farmers and researchers take action to prevent further damage
or the spread of disease.
Crop price predictor is a machine learning
model that helps farmers and traders make informed decisions about
buying and selling crops based on accurate insights about specific
regions and markets.
the crop recommender! Using n, p, k values, temperature, humidity, pH, and rainfall data, this machine learning model suggests which crops are most likely to grow and thrive in your area. With its help, you can optimize yields, increase profits, and promote sustainable agriculture practices. So, say goodbye to guesswork and hello to bountiful harvests with the crop recommender!
Your go-to destination for all the latest updates and advancements in the world of agriculture.
It scours news outlets and uses the news API to bring you the most relevant and up-to-date information on everything from new crop technologies to emerging trends in sustainable farming practices.
* Crop Yield Predictor: The crop yield model will leverage advanced algorithms to help farmers accurately predict their crop yields and enable them to make informed decisions regarding their farming practices. It will be powered by an advanced model that takes into account a wide range of weather and soil data. By analyzing these data points, the model can provide highly accurate predictions for a variety of crops.
* User Authentication: To ensure that our users' data is kept safe and secure, we'll employ industry-standard user authentication protocols to authenticate and authorize access to our platform. A secure connection between our webpage and machine learning models to prevent data breaches and unauthorized access will be maintained. This will ensure that our users can use the platform with confidence, knowing that their past activities and data are stored safely and securely.