Agritech 2024

Food security in the face of climate change and other challenges requires new ways of thinking about agriculture and how it works. Information asymmetry and lack of access to agricultural inputs (such as fertilizers), finance and markets have been challenges for decades. While there has been a strong focus on increasing production and productivity, the important element of reducing cultivation costs by optimizing resources has not received due attention. However, adopting a new approach requires that agriculture be seen and further developed as an agile, informed and dynamic sector that, like any other business, concentrates on optimizing resources, reducing costs, focusing on long-term sustainability and responding to market needs. The agritech sector is in the process of attempting to create this vision and offer services that respond to the needs of the hour and guarantee future food security. 1.1 Agritech: The situation today Intelligent crop planning Intelligent crop planning involves the use of AI-based models and other emerging technologies to create a detailed, market-oriented and sustainable macro- and micro-level crop plan that also responds to climate-change challenges and the nutritional needs of consumers. Taking into account advances in the sector, this focuses on: –Identifying crops that can withstand the challenges of climate change-induced disasters or weather-pattern changes while maximizing production and yield and managing risks –Using AI to predict gene-edited seed performance depending on different factors –Using data to develop models that can predict cropping patterns, harvest periods and production to gauge demand and price fluctuations more accurately –Targeting efficient nutrient management through soil testing –Accessing digital financial services, including credit, savings and insurance, to procure quality inputs, deliver agritech services and manage the risks in terms of climate change-induced disasters Use cases for intelligent crop planning are: –The gene editing of seeds using predictive AI tools: Gene editing is where adjustments in existing genes in plants are made; it is different from gene modification, in which external genes are inserted into an existing genome. The objective of gene editing is to drive higher yields with fewer or similar levels of existing resources. These changes are not made by altering just one or two genes but are the result of multiple complex gene edits. AI plays an important role in this process. Agritech innovators are using AI and predictive analysis to analyse plant genes and help create a plan for multiple gene edits. The use of AI allows scientists to work on this complex process more efficiently and with higher success rates. –AI-based soil-testing solutions: Soil testing at the early stages of crop planning is imperative to ensure better yields. Traditional soil-testing methods may take a few days or a few weeks, while a sensor and spectroscopy-based soil- testing kit might provide a report in near real time. However, the soil report itself is not the only outcome. Technology providers are also adding remedial actions based on the soil-test report in their modelling, allowing the farmer to take action based on the results. –AI tools to predict an optimal sowing period: Changes in weather patterns occur naturally; however, current weather patterns are attributed to climate change driven by human activity.10 Farmers have been adapting to these changes in their own environments – this is especially true for smallholders, given how many of them undertake subsistence agriculture. However, there is a need to provide farmers with standardized advice and information on ideal sowing windows that is based on predictions of weather patterns as well as the microclimatic conditions at a specific farm or cluster of farms. Solution providers may use a range of datasets, including previous weather data (temperature, precipitation), geospatial and hyperspectral datasets and microclimatic data through internet of things (IoT) sensors and microweather stations to generate advice for farmers. Studies have shown the benefits of choosing the right sowing window each season in order to maximize yield and minimize climate- related risks. –Augmented reality in crop planning: Augmented reality (AR) allows farmers to get a visual feel for the crop plan and layouts of their fields by overlaying digital information onto the physical environment in which they operate.11 AR- based crop modelling, although in an early phase of development and adoption, has the potential to scale if it can be developed as a service that can be used in an equitable way. Agritech: Shaping Agriculture in Emerging Economies, Today and Tomorrow 9