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Vegetation cultivated in aquaponics environments is prone to multiple diseases or biotic pressures throughout its development, similarly to conventional farming. Timely identification of these diseases is essential to maintaining the effectiveness of the aquaponics setup and advancing its development. Aquaponics merges recirculating aquaculture with soilless hydroponic cultivation and has the potential to ensure food security, alleviate water shortages, and reduce carbon emissions. To facilitate the widespread adoption of this agricultural method, an integrated platform is required to identify plant ailments and assist scientists and growers in identifying potential causes and remedies at an early stage. This research introduces an automated plant diagnostic framework for recognizing biotic pressures and controlling diseases in four leafy green vegetables—lettuce, basil, spinach, and parsley—cultivated within an aquaponics environment. Initially, a collection of 2640 images is assembled. Subsequently, a disease identification system operating in three stages is created. The initial stage involves a crop classification mechanism that determines the plant species. The second stage comprises a disease identification mechanism that assesses the plant’s health condition. The concluding stage is a disease-detection mechanism that pinpoints diseased and healthy areas on foliage and classifies the illness. The suggested method has yielded encouraging results, achieving accuracies of 95.83%, 94.13%, and 82.13% in the three stages, respectively. The ultimate disease-detection mechanism is then combined with an ontology framework via a cloud-based interface. This ontology framework includes domain expertise concerning plant pathology, specifically the origins and remedies for various ailments of the examined leafy green vegetables, which can be automatically retrieved upon illness detection, enabling farm operators to implement preventive actions. The suggested application is important as a decision-support tool that automates health surveillance in aquaponics facilities and helps agricultural practitioners make decisions on crop and disease control.

Computer vision and deep learning methods have attracted substantial interest in agriculture owing to their contactless, non-destructive nature. These technologies are increasingly being embedded in modern farming platforms, such as aquaponics, to tackle the barriers limiting their commercial expansion and industrial-scale deployment. Aquaponics is a production technique that fuses a recirculating aquaculture setup with soilless hydroponic cultivation, holding promise for addressing food security challenges. To supplement existing investigative efforts, a procedure is proposed to automatically quantify crop morphological features—namely, width, length, and area—and to infer the effective planting distance between grow channels. Planting distance is a core design variable that depends on crop type and its morphological expression; it therefore requires continuous surveillance to safeguard high yield and quality, which can be compromised by canopy occlusion or overlapping as plants develop. The presented pipeline applies Mask R-CNN to estimate crop dimensions and a mathematical formulation to determine plant spacing tailored to a self-adapting aquaponics installation. For common little gem romaine lettuce, growth is predicted within a 2 cm deviation for both length and width. The finalized model is served via a cloud-based platform and is coupled with an ontology model that encodes domain expertise in aquaponics systems. Pertinent knowledge regarding crop traits and ideal planting intervals is drawn from the ontology and cross-checked against the model’s outputs to propose follow-up measures. The introduced application derives its value as a decision-support framework, potentially paving the way for intelligent oversight and regulation.

Artificial intelligence is increasingly presented as a transformative force in agriculture, promising more precise decisions, better resource use, improved productivity, and more resilient farming systems. Yet much of this promise has been shaped around large-scale, capital-intensive, data-rich farms in high-income settings. This creates a structural mismatch between dominant agricultural AI imaginaries and the everyday realities of smallholder farming. Smallholder farmers operate under conditions that are often treated as barriers to technological adoption rather than as core design conditions. Limited connectivity, intermittent electricity, restricted access to smartphones, low disposable income, linguistic diversity, and uneven access to advisory support all shape how digital tools are encountered and used. When these realities are ignored, agricultural AI risks widening rather than narrowing existing inequalities. This article develops a Smallholder-Centred AI Model for agriculture. The model reorients agricultural AI around four foundational principles: low-resource design, inclusion and affordability, context-awareness, and farmer participation. It argues that these principles must not be added after technical development but must structure the design, governance, and evaluation of AI from the beginning. The article concludes that AI can serve smallholder agriculture only when smallholders are positioned as active participants in the intelligence of the system. A smallholder-centred approach requires technologies that work with limited infrastructure, respond to local agroecological and social contexts, respect farmer knowledge, and remain affordable and accountable. The future of agricultural AI must therefore be judged not only by predictive accuracy but by its capacity to support inclusive, locally grounded, and farmer-led transformation.