Shaping the Deep Tech Revolution in Agriculture 2025

Integration of edge IoT: Before and after FIGURE 6 Drivers and barriers to the use of edge IoT for agriculture FIGURE 7 Source: Consultations with AI4AI community experts AFTER Farmers, mostly those gr owing high-value cr ops, deploy IoT sensors for soil moistur e and weather data. These sensors upload all raw data to a cloud server for analysis. Connectivity drops during storms or in r emote ar eas, delaying r eal-time alerts for irrigation or pest outbr eaks. Farmers wait for long periods for r ecommendations, which ar e sometimes outdated when they arrive. On the technology side, service pr oviders r ely on subscriptions to large-scale cloud storage and computing capabilities, incr easing their operating expenses.With Edge IoT , sensors pr ocess data right at the farm level. This enables farmers to analyse soil, weather and cr op conditions without needing constant cloud access. The farmers get r eal-time, automated irrigation triggers or pest alerts even with weak connectivity . Decisions become faster , more precise and mor e resilient to network disruptions. At the same time, technology companies optimize their operating expenses by not having to subscribe to cloud storage or computing infrastructur e.BEFORE Increasing maturity of edge AI frameworks and operating systems: The development of lightweight machine lear ning frameworks and specialized operating systems designed for edge devices has made it easier to train and deploy complex models on edge har dwar e. Decreasing cost of IoT hardware: The continuous r eduction in the manufacturing cost of sensors, micr ocontr ollers and communication modules has made IoT economically feasible for smaller farms.Interoperability: Edge solutions thrive on communication and coor dination among a network of edge har dwar e devices. However , inter operability r emains a key challenge. Security challenges: While centralized clouds ar e easier to pr otect thr ough security models, r eplicating such models at the grassr oots level and at the “edge of the network” is challenging. This can significantly incr ease the cybersecurity risks and af fect pr oduction. Prohibitive costs: Although the costs of IoT have declined significantly combined with innovative payment models, the costs of integrating edge IoT , especially in emerging economies, ar e still pr ohibitive for most farmers, given their limited willingness and ability to pay .DRI VERS BARRIER S Growing proliferation of hardware as a service (HaaS) and software as a service (SaaS) models: Service models ar e lowering the initial capital expenditur e for farmers, making edge IoT solutions more affordable. These models typically r educe upfr ont capex to pr edictable operational expenses. Shaping the Deep-Tech Revolution in Agriculture 15