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Agricultural production is affected by plant diseases, affecting both quality and quantity. Yet, forecasting these diseases demonstrates its value for crop quality and reducing the chance of yield losses. The process of detecting plant diseases—whether by the naked eye or through conventional methods—is generally painstaking, time-consuming, and subject to substantial margins of error. This work presents a comprehensive exploration of diverse CNN architectures paired with distinct optimization algorithms, applied to olive disease identification via classification methods, and identifies the optimal model for an efficient disease detector. The exploration presents a collection of 5571 olive leaf images, gathered by hand in field settings across different regions of Morocco, and additionally includes a healthy class to identify olive diseases. Moreover, a primary aim of this research was to explore the interplay between CNN architectures and optimization algorithms as measured by accuracy and additional performance indicators. The peak score in the trained models reached 100%, while the peak score in experiments without data augmentation was 92.59%. A further emphasis of this exploration is the effect of optimization algorithms on neural network functioning. Based on the experiments, the MobileNet architecture with Rmsprop outperformed the other pairings in both detection performance and efficiency.

In light of ongoing trends in metropolitan farming and global climate shifts, there is a growing need for alternative plant cultivation strategies that can operate independently of soil. Aquaponic and hydroponic growing methods have proven workable replacements, yet the scarcity of streamlined, ideal procedures for watering and nutrient delivery curtails their deployment at a mass commercial scale. The primary objective of this investigation was to develop statistical techniques and Machine Learning models to adjust nutrient levels in aquaponic irrigation systems in response to crop demand, thereby securing peak plant performance and facilitating wider commercial uptake of aquaponic farming. A core difficulty in crafting these models is the thinness of available data, which calls for the application of Bolstered error estimation methodologies. This paper assesses a range of linear and non-linear algorithms conditioned on comparatively small data sets using bolstered error estimation techniques to identify the superior approach for guiding nutrient control decisions in hydroponic contexts. Upon conducting multiple tests on the data, it was concluded that the Semi-Bolstered Resubstitution Error estimation strategy yields the best results in our scenario, using a Linear Support Vector Machine as the categorizer with a penalty factor of 1. A series of suggested directives was developed as a Decision Support System, derived from the Machine Learning model’s outputs, and verified against the baseline framework’s findings. Moreover, the constructive influence of the recommended nutrient mixtures on plant growth in aquaponic conditions is examined in detail.

Digital twins are increasingly proposed as cyber-physical representations that may support precision agriculture through monitoring, simulation, prediction, optimization, and decision support. However, agricultural systems described as digital twins vary substantially in data quality, interoperability, synchronization, modelling depth, predictive reliability, decision functionality, validation, and operational governance. This article proposes a conceptual maturity framework that evaluates agricultural digital twins through capability progression rather than technology adoption alone. The framework begins with descriptive monitoring and advances through real-time farm representation, simulation and scenario analysis, prediction and optimization, and supervised autonomous decision support. Monitoring maturity depends on reliable sensing, remote sensing, Internet of Things integration, data fusion, spatial and temporal adequacy, traceability, and human-interpretable representation. Simulation and prediction maturity require explicit farm-system boundaries, calibrated models, uncertainty estimation, local validation, and evidence that analytical outputs remain valid for their intended context. Optimization maturity additionally requires transparent objectives, constraints, robustness assessment, and distinction between predictive accuracy and decision value. The highest proposed level permits only narrowly bounded and supervised decision initiation supported by human authority, explainability, safety mechanisms, governance, cybersecurity where relevant, and continuous feedback. The framework does not constitute a validated maturity standard or establish agronomic, environmental, economic, or autonomous decision benefits. Its principal contribution is an integrated structure for assessing capability readiness, identifying progression barriers, and preventing monitoring, simulation, prediction, or optimization functions from being overinterpreted as evidence of deployment-ready autonomy.