Agentic AI-Driven Cultivation Advisory and Symptom-Level Diagnostic Support in a Controlled Indoor Farming System (AgriEngineering 2026, 8(9), 350)

1. Introduction

The global demand for food continues to rise alongside population growth and increasing pressure on arable land, making food security one of the most pressing challenges of the twenty-first century [1]. Indoor farming has emerged as a promising alternative to conventional open-field cultivation, enabling year-round crop production in controlled-environment enclosures that are decoupled from seasonal variability, extreme weather events, and geographic constraints [2,3]. By precisely regulating light, irrigation, humidity, and temperature, indoor systems can achieve substantially higher yield consistency compared with traditional agriculture [4,5]. Despite these advantages, the majority of small-scale and urban indoor farming operations continue to depend on manual observation for plant monitoring and cultivation decision-making, resulting in delayed detection of stress conditions, inconsistent crop outcomes, and inefficient use of water and energy resources [6,7].

The integration of Internet of Things (IoT) technologies into agricultural systems has enabled continuous, automated monitoring of key environmental parameters, including soil moisture, temperature, humidity, and photosynthetically active light intensity [8,9]. IoT-based platforms can trigger automated actuation such as irrigation pulses and grow-light scheduling, substantially reducing the burden of manual intervention and supporting resource-efficient cultivation [10,11]. Recent deployments in smart greenhouse environments have demonstrated stable closed-loop control of temperature, humidity, and fertigation with measurable improvements in crop yield and resource use [12,13]. However, translating raw sensor streams into actionable cultivation guidance remains a significant challenge, particularly for non-expert users who lack the agronomic knowledge to interpret environmental readings or identify early signs of plant disease [14].

Recent advances in large language models (LLMs) and retrieval-augmented generation (RAG) have opened new avenues for intelligent advisory systems across a broad range of domains, including agriculture [15,16]. RAG-based agricultural chatbots ground LLM responses in domain-specific knowledge bases such as cultivation guides and pest management manuals, substantially reducing hallucination and improving factual reliability compared with vanilla LLMs [17,18]. Systems such as CottonBot [19] have demonstrated that RAG-augmented LLMs integrated with IoT soil-moisture sensors and weather APIs can deliver expert-level irrigation recommendations responsive to real-time field conditions. Similarly, PEZEGO [20] illustrated how LLM reasoning coupled with IoT telemetry can support pest management decisions in precision agriculture. However, most existing systems rely either on static knowledge bases without real-time sensor integration, or on single-task advisory pipelines without multimodal disease diagnosis capabilities. The concept of agentic AI, where an LLM autonomously routes inputs through condition-based pipelines and invokes external knowledge retrieval as part of its reasoning chain [21], offers a qualitative advance over standard RAG by enabling context-aware multi-step responses, yet end-to-end agentic deployments within unified indoor farming platforms remain largely unexplored.

Accurate and timely plant disease diagnosis is equally critical to cultivation advisory systems as undetected pathogens and nutrient deficiencies can severely reduce crop quality and yield [22]. Traditional diagnosis depends on expert visual inspection, which is both labor-intensive and inaccessible for non-specialist growers. Deep learning-based approaches have demonstrated strong performance in plant disease classification and segmentation tasks [23,24], and transformer-based models combining in situ sensor data with plant images have further improved diagnostic accuracy under realistic field conditions [25]. More recently, LLM-based knowledge graph construction for plant diseases [26] and multimodal vision-language models have shown the potential to support conversational, image-grounded diagnosis accessible to end users. Nevertheless, integrating such multimodal diagnosis capability within a real-time controlled indoor farming system, where automated closed-loop control and symptom-level diagnosis are delivered through a single natural-language interface, has not been demonstrated in the prior literature.

To address these gaps, this paper presents a system for agentic AI-driven cultivation advisory and symptom-level diagnostic support deployed in a controlled indoor farming system. The proposed system, built around Cos lettuce cultivation as the validation crop, integrates an ESP32-based sensing and control layer for real-time monitoring of soil moisture and grow light status together with automated closed-loop control of irrigation and LED grow lighting. An agentic LLM pipeline, grounded through an RAG pipeline constructed from domain-specific cultivation knowledge, employs condition-based routing and multi-step reasoning to deliver context-aware cultivation recommendations. A multimodal vision-language model provides symptom-level diagnostic support from user-submitted image and text inputs, enabling non-expert users to receive symptom interpretation and management guidance through a web-based conversational interface.

While the individual building blocks employed in this work are established open-source techniques and are not claimed as contributions in themselves, the contribution lies in the architecture that governs how inputs are routed between the text and vision paths, how responses are grounded in retrieved documentation and validated before delivery, and how the advisory layer is isolated from the control layer. Table 1 illustrates that existing systems address these capabilities in isolation, and the present work targets the identified gap.

Table 1. Design scope of representative IoT-, LLM-, and vision-based agricultural systems in relation to the proposed platform.

Two points of scope should be stated at the outset. First, this article reports a systems-engineering contribution. The research question is whether a platform integrating threshold-based actuation, on-device vision monitoring, and an agentic LLM advisory pipeline can be constructed and operated stably on commodity hardware without cloud dependency. Whether such a system improves agronomic outcomes relative to conventional management is a separate question, requiring a replicated and randomized cultivation trial with crop-performance measurement, and is not addressed here. Second, the term diagnosis is used throughout in the sense of symptom-level diagnostic support: the identification of visible symptoms, the enumeration of plausible differential causes, and the provision of corresponding management guidance. It does not denote pathogen confirmation, which requires microbiological or molecular verification and lies outside the scope of this study.

The main contributions of this work are as follows:

• Agentic advisory pipeline. An LLM pipeline combining condition-based routing, intent classification, RAG-grounded multi-step reasoning, and an LLM validation gate. It is specified as an explicit dual-path dispatch algorithm rather than a monolithic prompt.

• Multimodal diagnostic support. A LLaVA 7B module that interprets plant symptoms from images and text. It identifies visible symptoms, lists plausible causes, and returns management guidance for users without agronomic training.

• Browning detection. A vision routine pairing YOLO-based plant localization with color-ratio analysis to flag abnormal leaf coloration. Each alert logs the incident image, growth stage, and sensor state for follow-up.

• Isolated control layer. A threshold-driven architecture for irrigation and lighting actuation, separated from the advisory layer so that no language model output can reach an actuator (Section 3.1.6). Continuous unattended operation was demonstrated across a Cos lettuce deployment period.

The remainder of this paper is organized as follows. Section 2 reviews related work and positions the proposed system against the existing literature. Section 3 presents the materials and methods, covering the system architecture and design (Section 3.1) and the materials and experimental methods (Section 3.2). Section 4 presents the results. Section 5 discusses their implications, limitations, and directions for future research. Section 6 concludes the paper.

2. Related Work

This section reviews prior work across three themes directly relevant to the proposed system, closed-loop control in indoor farming, LLM- and agentic-AI-based advisories, and vision-based plant monitoring and diagnosis, and then synthesizes the recurring limitations that motivate the present integration.

2.1. IoT-Enabled Monitoring and Closed-Loop Control

Controlled-environment agriculture has been advanced substantially by IoT instrumentation that continuously senses soil moisture, temperature, humidity, and light and drives automated actuation [8,9,27]. Smart indoor and greenhouse platforms have demonstrated stable closed-loop regulation of temperature, humidity, and fertigation with measurable gains in yield and resource efficiency [4,10,12,28], and AI-assisted extensions have added fault detection [13], nutrient optimization [5], and low-cost sensing on commodity hardware [29,30]. These systems establish that reliable autonomous control is achievable on affordable hardware. While their primary focus is on sensing and actuation, the translation of raw telemetry into natural-language cultivation guidance remains an open challenge, particularly for non-expert operators [6,14]. The proposed system builds on this closed-loop foundation and adds a conversational advisory layer to address this gap.

2.2. LLM- and RAG-Based Agricultural Advisory

Large language models grounded through retrieval-augmented generation (RAG) have emerged as a means of delivering domain-specific advice while mitigating hallucination [15,16]. RAG-based advisory frameworks anchor model responses in curated agronomic corpora, improving factual reliability over vanilla LLMs [17,18,22]. Beyond static retrieval, a smaller body of work applies agentic designs, in which the LLM autonomously routes inputs, selects tools, and reasons over multiple steps. CottonBot [19] couples an RAG-augmented LLM with agentic tool invocation and IoT soil-moisture and weather sources to produce irrigation recommendations for outdoor cotton, and PEZEGO [20] pairs LLM reasoning with IoT telemetry for pest management. At a larger scale, Pardo-Pina et al. [21] propose a multi-agent service architecture for smart greenhouses with RAG-grounded LLM agents performing telemetry analytics and decision support. These systems demonstrate the value of integrating RAG and agentic reasoning for agricultural advisory systems. However, none incorporates image-grounded disease diagnosis alongside text-based advisories within a single pipeline. The present work extends this direction by combining dual-modality routing (text and image), intent classification, and output validation within a single pipeline, and by adding multimodal disease diagnosis as a complementary capability alongside cultivation advisory systems, deployed entirely on-device without external API dependency.

2.3. Vision-Based Plant Monitoring and Disease Diagnosis

Deep learning has become the standard for plant monitoring and disease recognition. Convolutional detectors and segmentation models achieve strong performance in disease classification [23,24,31], single-stage YOLO detectors have been applied to plant and crop localization across growth stages [32,33], and transformer-based models that fuse in situ sensor data with plant images further improve diagnostic accuracy under field conditions [25]; broader reviews document the rapid uptake of deep learning for plant-stress imaging [34]. More recent work explores LLM-driven knowledge representation for plant disease, such as knowledge-graph construction from the disease literature [26]. These approaches are primarily designed for recognition tasks and operate independently of cultivation control systems. In the proposed system, YOLO11 detection serves a dual role, localizing the plant for growth-stage classification and for region-constrained browning analysis, while a vision-language model makes diagnosis accessible through the same conversational interface that delivers cultivation advice.

2.4. Synthesis and Research Gap

Table 1 summarizes the preceding review along five capability dimensions that a non-expert-accessible indoor farming platform would ideally satisfy: autonomous closed-loop control, RAG-grounded advisory, agentic routing, multimodal image diagnosis, and cloud-free on-device operation. Existing systems each address a subset. Control-oriented IoT platforms provide autonomous actuation but no advisory intelligence; RAG and agentic advisory systems provide language-based guidance but omit multimodal diagnosis and depend on cloud infrastructure; and vision-based diagnosis models provide image understanding in isolation from control and advisory delivery. No prior system spans all five dimensions within a single deployment. The present work targets exactly this gap, integrating threshold-based control, agentic RAG-grounded advisories, and multimodal symptom interpretation on commodity hardware without cloud dependency, which is the integration this paper claims as its contribution.

A recurring constraint in this literature is that detection models deployed on low-power IoT nodes are typically restricted to compact architectures such as Tiny-YOLO in order to remain within the compute budget of the edge device, which limits achievable detection accuracy. The present work addresses this constraint through the placement of the inference workload rather than through further model compression: the detection model executes locally on a Raspberry Pi 5 acting as a supervisory node above the ESP32 sensing layer, at a five-second inference interval, so that a mid-capacity detector can be used without cloud offloading while the microcontroller retains responsibility only for deterministic threshold actuation.

3. Materials and Methods

3.1. System Architecture and Design

3.1.1. Overall System Architecture

The proposed system integrates three distinct architectural tiers into a unified indoor farming platform: (i)a physical sensing and control layer responsible for environmental sensing and automated actuator control, (ii)a backend AI intelligence layer running on the Raspberry Pi 5 comprising a FastAPI server, a YOLO11m plant-monitoring module, and a structured local data logging system, and (iii)a frontend layer hosted on a laptop comprising the Flowise-based agentic chatbot pipeline, Ollama LLM inference engine, and a React 19.2.0/Vite 7.2.5 web application accessible to the end user via a local network browser. Figure 1 illustrates the data-flow relationships among these three layers.

Figure 1. Three-layer s