The scale of the evidence base is striking. Following the PRISMA 2020 reporting framework, the team searched ScienceDirect, PubMed, IEEE Xplore, Google Scholar, and MDPI for English-language peer-reviewed work published between January 2021 and December 2025, retrieving 13,926 records. After deduplication, title and abstract screening, and full-text eligibility assessment of 198 articles, 95 studies survived the cut. Quality ran high: 51.58 percent of included papers appeared in first-quartile journals and another 36.84 percent in second-quartile venues, with Journal Citation Reports impact factors ranging from 2.2 to 14.0. The topical map shows where the intelligence is concentrating. Environmental monitoring accounted for 23.15 percent of publications, greenhouse control and protected cultivation for 21.05 percent, disease and pest detection for 21.05 percent, hydroponics for 16.84 percent, and yield prediction with production forecasting for 13.68 percent. Methodologically, classical machine learning anchored 25.26 percent of the studies, IoT and AIoT sensor systems 22.11 percent, and deep learning and computer vision 13.68 percent, a sign that pragmatic, sensor-driven systems currently outweigh frontier models in the field.

At the foundation sits machine learning, in which computers learn patterns from data rather than following explicitly programmed rules. The review organizes these algorithms into supervised, unsupervised, and reinforcement learning families and shows how workhorse models such as decision trees, support vector machines, random forests, and artificial neural networks now digest soil nutrient profiles, fertilizer inputs, and crop characteristics to make site-specific recommendations. In one line of work, random forests, support vector machines, and neural networks predicted optimal water requirements for maize, soybean, and tomato across diverse climates and soils, outperforming conventional irrigation scheduling. Transparency is emerging as a design priority. When researchers applied SHAP and LIME, techniques that reveal which variables drive a model’s predictions, to 15 greenhouse-grown cabbage plants tracked over an 85-day period, they could see that leaf count and plant height strengthened forecasts of nitrogen, phosphorus, and potassium levels while days after planting and average leaf area weakened them. Such explainable AI, the authors argue, is becoming essential for earning the trust of farmers expected to act on algorithmic advice.

Above that layer operates deep learning, multi-layer neural networks that automatically discover hierarchical features in raw data without hand-engineered rules. Convolutional neural networks, the workhorses of agricultural computer vision, learn to recognize edges, textures, and lesions in images streaming from cameras, drones, satellites, and field sensors, enabling real-time monitoring of plant health, growth analysis, and yield estimation. The review documents multimodal systems that fuse hyperspectral and X-ray imaging to predict seed viability non-destructively, alongside architectures such as VGG16, YOLO, Mask R-CNN, and transformer-based models that classify weeds, detect plant stress, and recognize growth stages. The techniques even reach into farm physics: one team built a three-dimensional computational fluid dynamics model to optimize airflow velocity, temperature distribution, and relative humidity across the cultivation trays of an indoor vertical farm, reporting improved airflow uniformity, better thermal control, and higher energy efficiency. Hybrid designs push further still. Convolutional layers coupled with bidirectional long short-term memory networks capture spatial patterns and temporal dynamics together, and a grey-wolf-optimized BiLSTM model forecast greenhouse temperatures with a coefficient of determination of 0.97.

Binding these computational layers together is the Internet of Things, networks of sensors, microcontrollers, and communication modules that measure soil moisture, temperature, humidity, pH, electrical conductivity, light intensity, and nutrient levels, then stream the data over Wi-Fi, LoRa, Zigbee, or cellular links to cloud and edge computing platforms. When AI sits atop those streams, researchers call it AIoT: sensors feed predictive models, models command actuators such as pumps, valves, LED arrays, and climate systems, and the farm adjusts itself in real time. The review catalogs the hardware ecosystem, from Arduino, ESP32, and Raspberry Pi controllers to LoRaWAN gateways and mobile dashboards. One LoRaWAN-based subsurface drip irrigation system, triggered when soil moisture fell to 12 percent, enhanced tomato seedling growth while cutting water use by 10 percent and lifting yield to 1,243 grams per plant. Edge computing is shrinking latency and energy costs further, and digital twins, virtual replicas of physical farms, now let growers simulate crop growth, microclimate behavior, and management scenarios before committing a single resource in the real world.

Nowhere is the transformation more visible than in controlled-environment agriculture, the umbrella term for greenhouses, vertical farms, and plant factories where light, temperature, humidity, and nutrition are engineered rather than endured. The review describes greenhouses that have evolved into quasi-autonomous organisms: one IoT-enabled system used artificial neural networks and regression models combined with image analysis to predict lettuce growth, harvest timing, and crop quality from environmental variables, while another deployed long short-term memory networks to forecast actuator behavior from live sensor data and adapt the climate automatically. Vertical farming, which stacks crops in layers to squeeze productivity from land-scarce cities, has embraced the digital twin approach, with one framework using a genetic algorithm to continuously retune RGB LED lighting against measured plant performance; the adaptive strategy consistently beat static configurations. Hydroponics, the soilless cultivation of roots bathed in nutrient solution, offers up to 90 percent water savings through closed-loop recycling, and machine learning and deep learning models now predict lettuce growth, flag abnormal plant conditions, and, with explainable AI, optimize even Thai basil production.

Water may be AI’s most dramatic success story. As climate change tightens the screws on global freshwater supplies, the review found smart irrigation systems consistently outperforming manual scheduling. An Arduino-based autonomous irrigation rig pairing soil-moisture and air-humidity sensors with an adaptive machine-learning controller achieved a water-use efficiency of 87.97 percent alongside higher plant survival, more uniform growth, and better visual quality than conventional watering. An AI-integrated framework combining IoT sensor networks with predictive analytics reached roughly 80 percent decision accuracy and simulated water-use efficiency gains of up to 25 percent, while a K-nearest-neighbors model predicting irrigation needs hit 98.3 percent accuracy in field trials. In a cloud-deployed greenhouse system for cherry tomatoes, models spanning multiple linear regression, support vector machines, random forests, extreme gradient boosting, and recurrent architectures predicted optimal irrigation with coefficients of determination around 0.82 to 0.83, translating into 17.8 percent heavier individual fruits and a 20.7 percent improvement in water-use efficiency under real growing conditions.

Computer vision is closing in on the pathogens, pests, and post-harvest logistics that drain harvests. An enhanced Faster R-CNN with multiscale feature fusion detected strawberry diseases at 92.18 percent mean average precision, and a pruned YOLOv5s model classified melon leaf diseases at 96.7 percent mAP with real-time inference. The most striking figure belongs to a hydroponic lettuce system called CNN-WOPNet, which married a ParNet-attention-enhanced convolutional network to a Walrus Optimization Algorithm and achieved 99.54 percent classification accuracy, 99.60 percent precision, and a 99.61 percent F1-score in identifying leaf diseases under extreme environmental conditions. Robotics is following the same trajectory. A tomato-monitoring robot using RGB-D cameras, LiDAR, and Faster R-CNN located fruit at 88.6 percent accuracy even with obscured samples, a YOLOv5-powered machine graded greenhouse mushrooms by size with 96 percent accuracy, and a vision-guided sowing robot cut vegetable planting operation time by 51 percent. Meanwhile, hybrid CNN-LSTM models fusing spatial and temporal features are sharpening soybean yield forecasts built from climate indicators and hydrological records, improving logistics planning and reducing waste.

But the review refuses to celebrate uncritically, and its quality appraisal exposes a soft underbelly beneath the headline numbers. Most included studies validated their models only through train-test splits or k-fold cross-validation on datasets from a single location, season, or production system; independent external validation, the gold standard for proving that a model generalizes, was rare. That means the published accuracies may overestimate real-world applicability. The authors also flag a metrics obsession: studies routinely report accuracy, precision, recall, F1-score, intersection-over-union, mean average precision, and RMSE, while computational efficiency, inference time, robustness under field conditions, and long-term operational reliability go largely unmeasured. Only a small fraction of the systems were ever tested in genuine agricultural environments, with many remaining at the laboratory or prototype stage. The team rated the overall risk of bias as low to moderate, stemming from dataset selection, controlled validation environments, limited external testing, and a publication bias that favors high-performing models, a reminder that failure cases, which matter most to practicing farmers, rarely make it into print.

The path forward, the authors argue, runs through hybridization and hardening. Multimodal systems combining machine learning, deep learning, and IoT still represent only 5.26 percent of the literature, yet they consistently post the strongest results, and AI-enabled edge devices are already delivering real-time pest and disease detection with high accuracy, low latency, and reduced energy consumption. Scaling those wins will demand standardized benchmarks, field-scale trials across locations and seasons, energy-efficient models that run on inexpensive hardware, explainability to win farmer trust, and robust data security for farms that have become, in effect, distributed computing networks. The review also ties the technology to crop genetics: AI-driven high-throughput phenotyping using RGB, hyperspectral, thermal, fluorescence, and 3D imaging is accelerating the identification of yield, stress, and disease-resistance traits that genomic tools can then target for breeding. What emerges is a portrait of agriculture in mid-transformation, no longer purely a craft of soil and weather and not yet a fully autonomous industry, but unmistakably becoming an information science. The success of that transition will help decide who eats in 2050.

The essence of farming by metrics lies in the systematic collection and analysis of data related to a wide range of agricultural inputs and outputs. Sensors embedded in fields, drones scanning crops from above, and satellite imagery feeding into precision agriculture platforms provide vast amounts of information previously unavailable. These technologies capture variables such as soil moisture, nutrient levels, crop health, greenhouse gas emissions, and water usage with unprecedented granularity. The operational capacity to monitor these parameters in near real-time enables farmers and agribusinesses to optimize resource use, increase productivity, and reduce environmental impact.

However, this rich data environment does not exist in a vacuum. The metrics chosen for focus and measurement are deeply embedded within governance frameworks, certification schemes, and market mechanisms that reward compliance with specific sustainability criteria. Public policies aimed at climate mitigation and resource conservation, alongside private sector sustainability commitments and consumer-driven certification standards, have converged to elevate particular metrics above others. This prioritization can inadvertently shape farm management by privileging certain production methods or crops that align with prescribed sustainability indicators, sometimes at the cost of other environmental or social factors.

The dynamic whereby metrics influence behavior is both empowering and cautionary. On the one hand, having clear, quantifiable targets provides actionable insights and accountability. It enables farmers to benchmark their practices, assess improvements, and communicate sustainability performance transparently to stakeholders. On the other hand, a narrow focus on measurable indicators risks oversimplifying complex agroecological systems and may marginalize less tangible but equally vital aspects like biodiversity preservation, soil health diversity, and socio-cultural values associated with farming.

Understanding the transformative potential of metrics in agriculture requires a nuanced perspective that acknowledges their dual role—as tools of measurement and as agents of change. They are not neutral arbiters but shape perceptions, priorities, and decisions within the food system. For example, when carbon footprint reduction becomes a key performance indicator, farmers may adopt practices like reduced tillage, cover cropping, or precision fertilizer application, which demonstrably lower emissions. Yet this focus can deprioritize other important issues such as water equity, labor conditions, or landscape-level ecological connectivity.

Technological integration further complicates this landscape. The deployment of advanced sensors and data analytics platforms often involves considerable capital investment and technical expertise, potentially disadvantaging smallholder farmers or those in resource-limited settings. There is a risk that farming by metrics could exacerbate existing inequalities if access to data-driven tools and insights remains uneven. Moreover, proprietary data systems and platforms may raise concerns about data ownership, privacy, and control over agricultural knowledge.

Beyond technical and ethical considerations, the metricization of farming also influences how sustainability is defined and communicated to broader audiences. Metrics translate complex environmental and social processes into simplified numerical scores or indices, which can shape consumer perceptions and market dynamics. Sustainability certification labels, built upon these metrics, wield significant influence over purchasing decisions, investment flows, and policy support. Consequently, the construction and validation of relevant, reliable, and inclusive metrics become pivotal activities in themselves.

The relationship between metrics and food system transformation is cyclic and reflexive. As metrics guide farming practices, emerging practices generate new data, prompting refinement of metrics and their underlying assumptions. This ongoing dialogue fosters innovation but also demands vigilance to ensure metrics remain responsive to ecological realities and community values rather than becoming static benchmarks that ossify particular models of production.

Critically, the broad adoption of metrics-driven approaches holds promise for accelerating the transition to more sustainable agroecosystems by enhancing precision and accountability. Yet it necessitates concerted efforts to broaden the scope of measurement to include multidimensional sustainability goals. This might involve integrating social indicators alongside environmental metrics, involving diverse stakeholders in metric development, and promoting transparency in data interpretation and use.

Such integrative efforts can help mitigate risks associated with reductive measurement and foster holistic sustainability transformations. They acknowledge that food systems must simultaneously address climate change, biodiversity loss, social equity, economic viability, and cultural heritage. By doing so, metrics become not merely technical tools but democratic instruments that enable collective stewardship and continuous learning.

Future research and practice should thus focus on designing metrics frameworks that are adaptive, participatory, and context-sensitive. This includes exploring hybrid models of qualitative and quantitative evaluation, developing open-access data infrastructures, and fostering farmer-centric innovation ecosystems. Importantly, governance actors—from policymakers to private sector leaders—must recognize their role in shaping metric agendas and ensure inclusivity and accountability in these processes.

In conclusion, the rise of farming by metrics represents a paradigm shift in how sustainability is operationalized within agriculture. It embodies a powerful convergence of digital innovation, policy ambition, and market transformation. However, unlocking its full potential requires critical reflection on which metrics matter, who defines them, and how they influence real-world farming decisions. Embracing this complexity is essential for steering the global food system towards resilience, equity, and sustainability in an era of unprecedented environmental and societal challenges.

Subject of Research: The role of data-driven metrics and sensor technologies in shaping and transforming sustainability practices in agriculture.

Article Title: The role of farming by metrics in transforming food systems sustainably.

Article References:

de Olde, E., Konefal, J. & Hatanaka, M. The role of farming by metrics in transforming food systems sustainably.
npj Sustain. Agric. 3, 40 (2025). https://doi.org/10.1038/s44264-025-00084-y

Image Credits: AI Generated