Shaping the Deep Tech Revolution in Agriculture 2025

Integration of computer vision: Before and after FIGURE 4 Drivers and barriers to the use of computer vision in agriculture FIGURE 5 Source: Consultations with AI4AI community experts AFTER Farm workers manually inspect cr ops for pests, diseases or ripeness. V isual checks ar e sometimes subjective and inconsistent, especially acr oss large or r emote fields. Missed signs of str ess lead to late interventions and yield loss. In the post-harvest stage, sorting and grading of fruits and vegetables r ely heavily on manual labour , which is slow , error- prone and costly for large volumes.Cameras, sometimes as simple as mobile cameras, ar e used to scan fields or gr eenhouses. Applications run algorithms that detect pests, diseases and nutrient deficiencies in r eal time with high accuracy , enabling faster , mor e precise interventions. Autonomous systems such as r obots use computer vision to navigate r ows, identify weeds or harvest ripe pr oduce. Furthermor e, grading and sorting systems grade pr oduce by size, colour or quality far mor e consistently and efficiently , saving time and r educing waste.BEFORE Declining sensor costs and improving access to mobile cameras: The availability of low-cost cameras is incr easing the compatibility and use of computer vision in r obots and autonomous systems for agricultur e. Concurr ently , impr oved access to smartphones has enabled farmer - centric use cases such as pest detection and disease identification.On-field variability: Unlike industrial pr ocesses, which can be consistent and uniform acr oss stages, agricultural pr oduction often demonstrates varied visual elements thr oughout a crop’s growth cycle. The ability to adapt models for agricultur e is challenging. Models trained under ideal conditions may often fail in agricultur e. Lack of training data: Like other ar eas of AI, developing computer vision use cases such as pest identification involves highly r obust databases with large r epositories of images of target subjects. Such databases ar e not r eadily available. High investment costs: The integration of computer vision still r elies on some form of capital investment, which may be challenging for smallholder farmers fr om emerging economies.DRI VERS BARRIER S Advances in deep learning models: Computer vision r elies on convolutional neural networks (CNNs); ther e have been several advances in CNNs over the past few years, enabling impr oved detection of a wide range of objects. Improvements in edge computing: Computer vision often r equir es the transfer of high- resolution images for impr oved accuracy . However , challenges in connectivity can r estrict this. Recent developments in edge computing have enabled close- to-farm pr ocessing of images and impr oved applicability for agricultur e. Shaping the Deep-Tech Revolution in Agriculture 13