• Priyanka Damale
• Sanket Panchal
• Pranit Gawand
• Payal Waghmare
• Prof. Sneha Deshmukh

Agriculture is considered to be one of the most important traditions practiced in India. But the very effect of climate change and its unpredictability has had a profound effect on agriculture. Predicting crop yields has therefore become a very important step in crop production and management. This paper proposes a brief analysis of yield predictions using machine learning algorithms such as Stacked Regression Algorithms. Early diagnosis is important for agriculture to have a good crop yield. Bacterial spot, late rot, Septoria leaf spot and yellow curved leaf diseases affect the quality of tomato plants. Automatic methods of distinguishing plant diseases also help to take action after detecting the symptoms of leaf diseases. This paper introduces the Convolution Neural Network (CNN) model for the diagnosis and diagnosis of tomato leaf disease.

Advance Reading, Feature Releases, Backbone, CNN, Diagnostics

"Crop Yield Prediction and Disease Detection using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.h620-h626, May-2022, Available :http://www.jetir.org/papers/JETIR2205879.pdf

"Crop Yield Prediction and Disease Detection using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. pph620-h626, May-2022, Available at : http://www.jetir.org/papers/JETIR2205879.pdf