From greenhouses to drones: How control algorithms reshape farming practices

Artificial intelligence (AI) and automation are reshaping agriculture, but the challenge of designing control systems that can cope with unpredictable farm conditions remains unsolved. A new study published in Processes takes a comprehensive look at how control algorithms are powering intelligent agriculture, outlining their current uses, comparative strengths, and future directions.

The paper, titled Control Algorithms for Intelligent Agriculture: Applications, Challenges, and Future Directions, reviews both classic and advanced control methods. It identifies where they are already applied in crop production, pest control, machinery, and resource management, while also flagging the obstacles that must be overcome before large-scale field adoption can be achieved.

How are control algorithms applied in modern agriculture?

Intelligent agriculture requires precise, real-time control of diverse systems ranging from greenhouse climates to autonomous tractors. Classic approaches such as proportional–integral–derivative (PID) control and fuzzy control remain widely used because they are inexpensive, simple, and robust for basic tasks. PID controllers are reliable in stable settings like irrigation systems, while fuzzy control is applied where uncertainty is high, such as adjusting greenhouse environments.

More advanced methods are increasingly being deployed to handle complex operations. Neural networks and deep learning models are used in pest detection and harvest sorting, where image recognition is crucial. Model Predictive Control (MPC) is applied to path tracking and greenhouse management, enabling high precision under dynamic constraints. Adaptive control, active disturbance rejection control (ADRC), and sliding mode control (SMC) are employed to stabilize machinery and optimize inputs under rapidly changing conditions. Multi-objective optimization methods are also emerging to balance competing goals such as yield, energy use, and wear on equipment.

Across five main application areas, crop production, pest management, agricultural machinery, resource optimization, and harvesting, the authors highlight real-world examples where these algorithms improve efficiency, reduce inputs, and raise sustainability.

What strengths and weaknesses do these methods show?

While advanced algorithms have proven effective in laboratory and pilot projects, the review underscores that each method carries trade-offs. PID is cheap and quick to implement but struggles with nonlinear and time-varying conditions typical of open fields. Fuzzy control handles uncertainty but depends heavily on expert-defined rules, which can introduce errors.

Deep learning excels at vision tasks such as pest recognition and crop quality grading, yet requires vast labeled datasets and heavy computational resources. Model Predictive Control can achieve centimeter-level accuracy for path following but is computationally intensive, limiting its use on low-power farm machinery. Adaptive control, ADRC, and SMC offer resilience against disturbances but can be difficult to tune or may suffer from chattering effects.

Multi-objective optimization provides a structured way to balance sustainability with efficiency, but choosing among Pareto-optimal solutions can be subjective and slow. No single algorithm offers a perfect solution across all tasks, making hybrid approaches increasingly attractive. Combining classic methods with AI can help bridge the gap between speed, robustness, and computational demands.

The authors argue that the comparative analysis makes clear that deployment decisions must consider both the technical environment and the constraints of agricultural practice. A greenhouse with stable conditions requires different tools than an open field exposed to weather, dust, and variable soil.

What challenges and future directions lie ahead?

The review identifies a series of barriers preventing control algorithms from moving seamlessly from research into widespread farm adoption. Environmental complexity and unpredictability remain the greatest hurdles, as algorithms tuned in controlled conditions often fail in fields exposed to dust, glare, rain, and mechanical vibration. Hardware limitations are another barrier: many farms cannot afford high-end processors capable of running advanced algorithms, creating a demand for lightweight, edge-ready models.

Data scarcity is also a pressing problem. Deep learning systems in particular need large, labeled datasets, which are expensive and time-consuming to collect in agriculture. Real-time demands create further pressure, especially in autonomous machinery where delays of even milliseconds can cause errors. Integration across multiple goals, such as yield, cost, and sustainability, remains a challenge, as does ensuring human operators can trust, understand, and easily use the systems.

The authors suggest several directions for further progress. Lightweight algorithms optimized for edge devices are urgently needed to bring intelligence to low-cost machinery. Hybrid models that combine PID or fuzzy control with AI promise to balance robustness with adaptability. Digital twin technology could allow developers to test systems in virtual environments before deployment, lowering risks and costs. Multi-sensor fusion, integrating data from cameras, LiDAR, GPS, and soil sensors, could strengthen reliability in real-world environments. Lastly, sustainability must become a core design goal, ensuring that control systems not only maximize yields but also minimize water, fertilizer, and energy use.