Our solution is smart and intelligent to provides timely, context-specific information that enables smallholder farmers and those who support them to identify, suggest treatment, track and share information about incidences of pests and diseases on African farms and gardens.
It uses machine learning on Android to help farmers fight pests and disease affecting their crops. It is designed to work both on and offline.
Pests and diseases on African farms not only cause a threat to food security but also to the economic prosperity of farmers. According to the report commissioned by the Department for International Development(DFID) in September, 2017, the arrival of fall armyworm (FAW) in Africa had the potential to cause maize yield losses in arange from 8.3 to 20.6 million tonnes per annum, in the absence of any control methods, in just 12 maize-producing countries. This represents a range of 21%-53% of the annual averaged production of maize over a three year period in these countries. The value of these losses was estimated between $2,481m - $6,187m. The same worm can affect 80 plant species.
Our work got recognized by Google Developers and was featured at their biggest developer conference called Google IO in 2019. Here is a video that was done by a team sent by Google to cover the story.
We participated in the Hack against Hunger competion orgaized by the Food and Agriculture Orgination of the United Nations (FAO) which happened in Kigali, Rwanda in 2018 and we emerged as the best technical team despite taking third position overall.
Our participation and competance got us to participate in the youth innovation sympozium organized by FAO in Rome Italy in 2018. We got featured in several publications that FAO has done bellow. You click on any for more detail.
1. Hack for Harvests : How drones and smartphones are helping Ugandan farmers save their crops from pests and diseases
2. Proceedings of the International Symposium on Agricultural Innovation for Family Farmers - Unlocking the potential of agricultural innovation to achieve the Sustainable Development Goals. check on page 105.
3. HACK FOR HARVESTS
We also make a guest post on Google Developer Blog titled:
4. Using machine learning to tackle Fall Armyworm
Two thirds of the population in poor countries depend on agriculture for survival. 30-40 % of agriculture yields are lost due to pest, diseases and post-harvest loss every year causing a big threat to food security to the population. This idea when implemented will lead to an improvement in yields due to early detection and treatment of crops from pests and diseases. The information collected about the detected and treated pests, diseases and crops with farmers’ consent will be data mined and used for future predictions of when outbreaks are likely to occur, which areas are more susceptible, etc. This will lead to minimized or elimination of these pests and diseases hence no hunger. Time to know and acquire the pesticides and advice will be reduced because the solution will not only suggest the best pesticide and action to take but also the nearest possible store to purchase or obtain the pesticide.
Our solution uses TensorFlow, an open source software library for high performance numerical computation with a flexible architecture that allows easy deployment of computation across a variety of platforms. TensorFlow comes with strong support for machine learning and deep learning with a flexible numerical computation core that is used across many other scientific domains.
We collected image data of pests and crops featuring signs and symptoms of particular disease or pest infestation. Data collected was carefully sorted and processed. The well sorted and processed data was used in training a deep learning model. With a trained model available, it integrated in a mobile application that is used on low end smartphones to deliver the functions. The mobile application uses the vast capabilities and items onboard like sensors and camera to deliver the targeted experience.
A number of challenges were and still are affecting this agricultural innovation, below are the salient ones. Lack of resources to collect enough training data and high spec equipment to use the collected data to train a machine learning model. Many smallholder farmers do not have smartphones to use the solution The team did not have experts like agronomists, pathologists at the time, we have just gotten And now formalizing a co-working relationship with Buginyanya Zonal Agricultural Research and Development Institute (BugiZARDI). Lack of access to good equipment during development is another constraints