AgriSense

Manually Testing using breadboard
Crop Doctor
Dashboard
Home page
Use-case diagram
Wireframes

Inspiration

Inspiration for the idea is based on how easily farmers in agriculture are easily challenged to cope with crop management. Most of them cannot monitor environmental factors that cause disease in crops early enough, which causes damage by pests and animals as well. Soil degradation and the need for timely intervention demand modern solutions. We came up with a system that uses technology to give farmers rights to automate these processes in order to make farm management run more efficiently and reduce the amount of manual intervention. Crop protection will be made smarter, more affordable for farmers so that they can maximize yields and protect crops.

What it does

Our AgriSense tracks the real-time values of environmental factors such as NPK values, moisture levels in the soil, temperature, humidity, and the pH level. It captures and foresees crop diseases using input images picked up by the ESP-32 cam module in combination with sensor data coming from hardware devices. It's processed and then made visible on an easily understandable dashboard through graphs and charts. There are other features here including Fertilizer and Crop Recommendations, Automatic Irrigation, Hazard Detection, and a Community Forum. A mobile app is also supplied so that farmers can utilize their own smartphone to identify diseases.

How we built it

We developed a web and mobile application that enables high-quality collection and displaying of data. Real-time environmental data coupled with pictures from the ESP-32 cam module is sent to a microcontroller and then to our database. We then process the whole process that was involved in the collected data and visualize it on our website, which enables fast decision-making. The mobile application complements the web platform, capturing crop images and receiving disease predictions directly on the phones of farmers. We have used various hardware sensors to capture environmental data so that the system may be robust in its predictions and effective management of crops with regard to diseases.

Challenges we ran into

We had the major challenge of hardware and software integration in a way that real-time data processing is present without any delay. The other was managing high volumes of data from sensor inputs combined with accuracy for processing. Extensive testing and fine-tuning were required while building a comprehensive model about disease prediction based on environmental data and inputs via images. We optimized data transmission and storage to be able to overcome the last challenge that we were able to undertake in designing a working mobile app for rural areas with limited connectivity.

Accomplishments that we're proud of

We are proud to develop an end-to-end solution that automates many of the critical aspects of farm management, starting from real-time environmental monitoring, disease detection, and covering a platform for giving actionable insights to farmers. Another major achievement on our part lies in our ability to integrate hardware sensors with a web and mobile interface. Moreover, we have the pride that AgriSense is available and affordable; thereby, it presents a tool that farmers can utilize in order to increase the productivity levels while reducing operational costs. Community features and an integration with chatbots further enhance usability and impact.

What we learned

Throughout this project, we learned the significance of seamless integration between the hardware and software aspects in a farming environment where real-time data can often be critical. Therefore, an aspect that opens our eyes to this correlation is the data from the environment in terms of crop health and how a machine learning model could be applied in predicting diseases. We learned the importance of the interfaces being user-friendly as farmers need such intuitive systems with clear insights without the technical nuisances associated with information. Besides this, we gained more insight into the tremendous pain points farmers are facing presently and how technology can solve these.

What's next for AgriSense

Future Development Moving forward, we intend to improve AgriSense by adding predictive analytics capabilities through historical data and real-time input so that this can give a reasonable estimate of crop health and yield. We also expand the reach of this platform by adding support for more languages and introducing drone-based monitoring for the larger farms. Improvement on disease detection will be improved to increase the accuracy and sensitivities of early identification. We continue to develop our mobile app in being accessed by farmers in inaccessible areas, and we're looking towards partnerships that will help scale AgriSense further up to other places.