Smart Farm Diagnostics: AI-Driven Yield Forecasting and Advisory Platforms

Thesis Abstract

The rapid adoption of digital agriculture and the increasing availability of sensor-enabled devices have generated vast amounts of farm-level data, yet farmers often lack timely, actionable guidance to translate this information into optimal management decisions, leading to suboptimal yields and resource inefficiencies. This study addresses the problem of fragmented data ecosystems in smallholder and commercial farming by developing an integrated AI-driven diagnostic platform that combines real-time sensor inputs, historical agronomic data, and climate forecasts to produce accurate yield forecasts and field-specific advisory recommendations. The aim is to design, validate, and evaluate a scalable Smart Farm Diagnostics system that (i) forecasts yield at field and parcel levels with probabilistic uncertainty, (ii) delivers context-aware management advisories (e.g., irrigation, fertilization, pest management) via a mobile interface, and (iii) supports extension services and market decision-making through an analytics dashboard. Specific objectives include (1) to collate and harmonize heterogeneous data streams from soil, weather, remote sensing, and crop phenology sources; (2) to develop machine learning models for yield prediction using supervised learning (random forests, gradient boosting, and deep learning architectures) and incorporate uncertainty quantification through Bayesian methods; (3) to implement an explainable AI (XAI) framework that translates model outputs into farmer-friendly advisory messages and risk assessments; (4) to evaluate model performance across diverse agronomic zones using cross-validation and out-of-sample testing; (5) to assess the system’s impact on input efficiency, yield variability, and decision-making processes through a mixed-methods field trial; and (6) to examine stakeholder acceptance and ethical implications of data sharing and algorithmic recommendations. The methodology adopts a pragmatic, multi-site research design. The population includes 200 commercial and 400 smallholder farms across three agro-ecological zones. A stratified random sample of 100 farms will participate in iterative pilot deployments of the platform over two cropping cycles. Data collection instruments comprise (i) IoT soil moisture and environmental sensors, (ii) drone- and satellite-derived vegetation indices, (iii) agronomic management logs, (iv) meteorological forecasts, and (v) structured interviews and focus groups with farmers and extension workers. Instrument validity and reliability will be established through pilot testing, Cronbach’s alpha for survey items, and inter-rater reliability for qualitative coding. Data analysis will integrate quantitative and qualitative approaches predictive modeling will employ gradient boosting machines, recurrent neural networks, and Bayesian hierarchical models to forecast yields with prediction intervals; variable importance and SHAP values will ensure interpretability; economic analysis will use counterfactual simulations to estimate potential returns from optimized advisories; and thematic analysis will synthesize farmer experiences and adoption barriers. A conceptual model drawing on the Technology Acceptance Model (TAM) and the Diffusion of Innovations theory will guide interpretation of adoption dynamics, while Event History Analysis will explore advisory action timing relative to weather shocks. Expected findings include (i) improved yield forecasting accuracy with 15–20% reduction in RMSE and credible intervals capturing observed variability; (ii) significant reductions in water and fertilizer use without yield penalties, evidenced by a difference-in-differences estimation; (iii) higher farmer satisfaction and perceived usefulness driven by explainable recommendations; and (iv) enhanced decision-making efficiency as indicated by time-to-action metrics and adoption rates. The study contributes to knowledge by integrating AI-driven diagnostics with agronomic advisories in a quantifiable value chain context, advancing methodological clarity on multi-modal data fusion, uncertainty quantification, and user-centered design in intelligent agricultural decision-support systems. The main conclusion is that a context-aware, transparent AI advisory platform can meaningfully improve yields, resource use efficiency, and farmer decision-making under variability in climate and market conditions. Policy and practice implications highlight the need for data governance frameworks, scalable cloud-based architectures, and capacity-building programs to support widespread adoption, with recommendations for iterative platform refinement, open data standards, and collaboration with agronomic researchers and extension services.