Agriculture is one of the main economic resources in Egypt. The losses in our agricultural production every year are because of the agricultural diseases. Crop diseases are one of the most important factors currently that affect agricultural production and food security. Fungicide utilizer is a way to integrate technology with agriculture to help advance the agriculture sector. Fungicides, pesticides, and weather are factors that affect crops in many different ways. In order to overcome these problems, our project tends to build a weather station system to help measure weather impacts and gather data for analysis in attempts to help farmers avoid disease threatening crops with a model that predicts disease timing with appropriate timing for fungicides or pesticides. Based on weather station readings and experiments, we aim to build a Machine learning special transfer learning model that predicts diseases that may affect crops such as corn, potatoes , tomato , and apple . Also , A fuzzy model technique that predict disease for wheat  such as all kinds of rust, leaf diseases, insect pests, fusarium head blight (FHB) and fungal diseases by tracking them.

To help farmers improve and ensure crop quality.

To help researchers track some crop diseases and build assumptions based on forecasting the weather.

To detect wheat rust disease in its early stage.

To increase the percentage of wheat production in Egypt.

Check for some disease for potato, tomato, Corn, Apple crops 

The proposed system is designed to:

Build a weather station that collects data for analysis for many uses, with an Internet of Things system which tells the farmer when to apply fungicides and the dose percentage.

Build a fuzzy model to help detect rust disease in the wheat crop early.

Detect crop diseases through image classification

Predict weather related diseases

Calculate fungicides efficiency 

Keep track on land status

View info about crops, diseases and fungicides