Real Time Rapid Response System For Fall Armyworm
Growing up I had many heroes, on top of the list however were the power rangers, especially the “Green Ranger”. The one who always came to the rescue of all other rangers when help was vain. And to add ketchup to the fries was the fact that whenever he showed up there was an idiosyncratic soundtrack to his appearance. That would complete the whole experience for me. There is something about admiration that inspires our affections, and because it inspires our affections, it speaks into our actions and thus my passion for “Green” plants with a fulcrum of Crop Protection. The fuel of my passion is the incomparable value crops add to society; the furnace of my passion is hinged on the untimely detection of the endemic Fall armyworm (FAW), and the heat of my passion is the inefficient field scouting methods that have failed to control FAW.’
Crop protection has come a long way since Egyptian farmers first used the scarecrow, some 5,000 years ago. Every year, according to the Food and Agriculture Organization of the United Nations, as much as 40 percent of the world’s potential harvests are lost to damaging insects, weeds, and plant diseases. Farmers today have an incredible array of tools to protect their crops from these threats. Some of them have been used for centuries. Others have developed more recently. The key to the effectiveness of these tools is how farmers use them in concert with one another. Throughout the history of agriculture, each new wave of crop protection innovation allowed farmers to be more efficient. Tillage reduced the need for hand weeding. Chemicals reduced the need for tillage. Genetically modified seeds reduced the need for insecticides. Data analytics, combined with precision planting and spraying techniques, has made farmers even more efficient, helping them farm with less of an impact on our environment.
To date, the development and implementation of coordinated, evidence-based effort to control the FAW in Africa have faced a number of challenges. In particular, FAW is a recently introduced pest in Africa. Therefore, FAW scouting by farming communities and effective monitoring at the country, regional, and continental levels are limited. In addition to delaying the recognition of the pest’s movement through Africa, this lack of surveillance, monitoring, and scouting capacity has delayed efforts to determine several key unknowns about FAW populations on the continent and the dynamics of the pest’s establishment and spread. The lessons learned from the invasive FAW pest should be identified quickly because they are important for monitoring and interception of future invasive pests.
Being an exceptionally meticulous and reliable Plant Breeder with a superb record of accurate and consistent scientific work with biotechnology as my fulcrum, I would love to develop a Real-Time Rapid Response system (RTRRS) matched to FAW in Maize (To quickly detect the armyworm/moths and effect quick relief measures) as a case study, better exploiting the next generation innate resistance technologies; and demonstrate a safe mode of actions and benefits that might help new crop protection products.
The main objective is to provide quick practical solutions to reduce dependence on the manual field scouting and spraying in selected major farming systems in East and Central Africa, thereby contributing to increased incomparable efficiency in routine scouting to identify and respond to potentially damaging pests (FAW) infestations when they occur the implementation of the next-gen crop protection while ensuring continued food production of sufficient quality.
The core of the system will be its ability to quickly detect the presence of this pest through the use of remotely controlled drones that periodically scout different sections of the field while taking images of the plants from various angles. These geo-tagged images will then be uploaded to a cloud-based deep learning artificial intelligence that will give an indication of which parts of the field are more likely to be infected by the armyworm and also predict how it might spread.
This information is then sent back to the control centre at the farm that will have the ability to quickly initiate a response mechanism through sending emergency drones equipped with pesticides and/or automatically activating remote pesticide dispensers placed within different areas in the farm, connected through wireless sensors. The deep-learning algorithm used will be initially trained to identify possible infected plants from previous images taken both at night and during the day. It will be trained to detect the armyworm at different stages of its life cycle (eggs, larvae, pupa, and adult stages) in different conditions like day, night, and rainy conditions The drones will be equipped with high resolution 4K noir cameras (capable of night vision if needed) and will be sizeable enough to carry a specific dose of pesticide as an emergency response to the pests. In the event that an infection is detected at a confidence level higher than 70% in a particular area in the field, the control centre will first issue an alert to the farmers about the danger detected through a text message and an application notification. The farmer will then have the option to select various countermeasures of dealing with the alert from an application running both mobile and at the control centre. The control centre can then wirelessly issue out a command to all remote dispensers (Pesticide Chemigation System) to quickly spray nearby infected plants with a pre-set dose of pesticide depending on the level of infection as prescribed by the farmer. The control centre can then launch remotely controlled drones to also aid in spraying the infected areas. As a result, the farmer can have enough time to prepare and launch an all-out assault on the worms before the infection spreads to severely damage the crops.
