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Reliable estimation of crop yield is vital for refining farming operations and guaranteeing food supply. This investigation tested the capacity of machine learning techniques to estimate tomato yield using meteorological measurements, spectral reflectance bands, and vegetation indices subjected to different nitrogen levels and bio-stimulant applications to generate variation in crop development. UAV-mounted sensors captured spectral information across seven acquisition dates spanning October 27 to December 15, 2023, matching critical developmental windows: vegetative establishment (acquisition 1), anthesis (acquisitions 2 and 3), fruit set and enlargement (acquisitions 4, 5, and 6), and initial maturation (acquisition 7). Discriminating explanatory variables were selected via the Pearson correlation metric (r > 0.65, P < 0.05), incorporating Near Infrared (NIR), Red Edge, and Red wavebands, together with vegetation indices comprising NDVI, GNDVI, NDRE, and SAVI. Remotely sensed canopy signatures gathered throughout the fruit set and enlargement window (acquisitions 5 and 6) displayed the tightest association with harvested yield (r = 0.66–0.74), reinforcing the necessity of spectral intelligence from the middle to latter parts of the cycle. When comparing predictive algorithms, both linear regression (LR) and XGBoost yielded the strongest predictive skill, producing root mean squared error (RMSE) figures of 16.13 kg and 16.15 kg, respectively, matched with R² values reaching 0.63. Support vector machine (SVM) and decision tree (DT) implementations also delivered competent outcomes, achieving RMSE scores of 17.15 kg and 17.18 kg, respectively. By comparison, the deep learning architecture lagged notably (RMSE = 23.49 kg, R² = 0.23), plausibly attributable to data scarcity. This research spotlights the prognostic capability inherent in spectral wavebands and underscores the centrality of growth-stage-aligned spectral acquisition for yield forecasting.

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.

Explainable artificial intelligence is becoming central to precision agriculture because farm-management decisions increasingly depend on algorithmic recommendations generated from sensor, remote-sensing, weather, soil, crop, and machinery data. However, technical performance alone does not make agricultural AI trustworthy. A recommendation may be statistically accurate yet remain unusable if farmers cannot understand why it was produced, how uncertain it is, whether it fits local conditions, or how it can be questioned. This article develops a farmer-centered approach to explainable AI for precision agriculture. It argues that explainability must connect model logic with farmer interpretation, contextual judgment, decision confidence, contestability, accountability, and feedback learning. The proposed perspective treats farmers not as passive recipients of algorithmic advice but as active interpreters, evaluators, and co-decision-makers within human-in-the-loop agricultural systems. By reframing explainable AI as a socio-technical decision-support requirement, the article contributes a foundation for trustworthy algorithmic decision support that is understandable, usable, context-sensitive, and accountable in real farm-management conditions.