







































Minkuan Zhang /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 
26-37 

26 

 

 

 

Article 

Multi-source field sensor data fusion based on cross 

modal attention mechanism and reinforcement 

learning driven pesticide application optimization 

model: towards sustainable crop protection 
Minkuan Zhang* 

Department of Biology, Faculty of Science, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia 

A R T I C L E   I N F O 
 

Article history: 
Received 17 August 2025  
Received in revised form 
24 September 2025 
Accepted 06 October 2025 
 
Keywords:  
Cross-modal attention, Reinforcement learning, 
Multi-source sensor fusion, Precision agriculture, 
Sustainable crop protection 
 
*Corresponding author 
Email address: 
minkuan0316@gmail.com 
 
 
DOI: 10.55670/fpll.futech.5.1.3 

A B S T R A C T 
 

The intensification of global agriculture demands precise and sustainable pest 
management strategies, as indiscriminate pesticide application continues to 
cause environmental degradation and reduce crop resilience. Existing 
approaches often rely on unimodal sensing or static rule-based spraying, which 
fail to capture the heterogeneous and dynamic nature of crop-pest-
environment interactions. To address this limitation, we propose a multi-
source field sensor data fusion framework that combines a cross-modal 
attention mechanism with a reinforcement learning-driven model for 
optimizing pesticide applications. The method integrates Unmanned Aerial 
Vehicle (UAV) hyperspectral imagery, soil and weather sensors, and pest 
monitoring signals through adaptive attention, encodes temporal dynamics 
with recurrent structures, and optimizes spraying actions via a PPO-based 
policy network. Experiments across rice, maize, and soybean datasets 
demonstrate superior performance, achieving the lowest RMSE (0.162), highest 
spray precision (88.3%), and notable pesticide reduction (18.3%) compared 
with state-of-the-art baselines. These findings highlight the potential of cross-
modal AI and adaptive control to advance sustainable crop protection, 
providing a scalable paradigm for intelligent agriculture. 

1. Introduction 

The modernization of agriculture increasingly relies on 
intelligent technologies to ensure sustainability, food 
security, and ecological balance. Precision pesticide 
application has become a critical focus, as excessive or 
misdirected spraying not only elevates production costs but 
also contaminates soil, water, and air, thereby posing risks to 
biodiversity and human health [1]. With the proliferation of 
Internet of Things (IoT) devices and advanced sensing 
technologies, field conditions can now be monitored through 
diverse modalities such as hyperspectral imaging, soil 
moisture probes, weather stations, and pest detection 
systems [2]. Integrating these heterogeneous data sources 
into a unified framework is essential for accurately capturing 
crop health dynamics and supporting real-time decision-
making for pesticide use [3]. However, translating such multi-
source field data into actionable spraying strategies requires 
both effective fusion techniques and adaptive optimization 
models that can operate under uncertainty [4]. Despite 
notable progress, existing approaches face several challenges. 
Traditional decision-support systems typically employ rule-

based thresholds or simplistic regression models that cannot 
capture complex interactions among diverse environmental 
signals [5]. Machine learning models have improved 
predictive accuracy but often depend on single-modality 
inputs, leading to limited generalizability across varying field 
conditions [6]. Moreover, most optimization strategies 
remain static, overlooking the dynamic nature of pest 
outbreaks, microclimatic fluctuations, and crop growth 
cycles. Reinforcement learning has recently been applied in 
agriculture, yet current studies often rely on simulated 
environments or simplified datasets, resulting in limited 
robustness when deployed in real-world conditions [7]. 
These limitations highlight the urgent need for a framework 
that simultaneously integrates heterogeneous sensor data, 
models cross-modal relationships, and adaptively optimizes 
pesticide application. To overcome these gaps, this study 
introduces several key innovations. First, a cross-modal 
attention mechanism is employed to dynamically weight 
heterogeneous sensor streams, ensuring that the most 
informative signals, such as spectral indices during pest 
outbreaks or soil parameters during drought, are prioritized. 

 

 

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Minkuan Zhang /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 26-37 

27 

 

Second, reinforcement learning-driven optimization is 
incorporated to enable adaptive decision-making under 
uncertain and temporally varying field conditions, moving 
beyond static spraying rules. Third, a multi-source data fusion 
framework is designed to unify spectral, climatic, and soil 
data, thereby providing a holistic representation of the field's 
status. Finally, a robustness-oriented evaluation protocol is 
implemented to test system stability under noisy sensor 
conditions, ensuring real-world applicability. Each innovation 
addresses a specific limitation of existing research and 
contributes to both methodological advancements and 
agricultural practices. Empirical validation on multi-season, 
multi-site datasets demonstrates the effectiveness of the 
proposed framework. Compared with state-of-the-art 
baselines, including CNN-based spectral models, RNN-driven 
temporal predictors, and rule-based agricultural decision 
systems, the proposed model improves pesticide application 
precision by 14.7%, reduces chemical use by 18.3%, and 
enhances pest suppression by 12.5%. Furthermore, 
robustness tests show that even under 20% artificially 
injected sensor noise, performance degradation remains 
below 5%, outperforming competing methods by a wide 
margin. These quantitative results confirm not only the 
superiority of the proposed approach but also its practical 
potential for reducing ecological impacts while sustaining 
crop yields. From an academic perspective, the model 
contributes to cross-modal learning and reinforcement 
learning in agricultural informatics, while from an applied 
perspective, it provides a viable pathway toward smart, 
sustainable crop protection. 

Existing multimodal models, such as AgriTransformer, 
excel at perception but stop short of closed-loop decision 
making; conversely, RL spraying systems (e.g., DRL-Spray) 
optimize actions but rely on single-modality or weak fusion, 
limiting robustness under field heterogeneity. We contribute: 
(i) a cross-modal attention with modality dropout that learns 
context-dependent sensor weighting and tolerates 
missing/noisy streams; (ii) a PPO policy trained on real multi-
season, multi-site data with on-policy feedback signals tied to 
agronomic outcomes; (iii) a reward elicitation protocol with 
domain experts + sensitivity analysis ensuring non-arbitrary 
trade-offs; (iv) an interpretability pipeline mapping attention 
patterns to farmer-actionable insights; (v) comprehensive 
fair-tuning and significance testing across strong baselines. 
The remainder of this paper is structured as follows. Section 
2 reviews related works on precision agriculture, data fusion, 
and reinforcement learning applications. Section 3 details the 
proposed methodology, including the problem formulation, 
framework design, module architecture, and optimization 
strategy. Section 4 presents experimental setup, baseline 
comparisons, quantitative and qualitative analyses, 
robustness evaluation, and ablation studies. Section 5 
discusses the findings, limitations, and broader implications. 
Section 6 concludes the study by summarizing contributions 
and outlining future research directions. 

2. Related works 

2.1 Application scenarios and challenges 
In precision agriculture, typical tasks include pest 

detection and classification, crop yield prediction, disease 
detection, pesticide residue detection, and optimization of 
pesticide/fertilizer spraying [8]. Data in these tasks often 
comes from multiple sensor modalities: 
optical/hyperspectral imagery, multispectral and RGB 
cameras, soil moisture, weather/climatic data (temperature, 
humidity, rainfall, wind), sometimes Light Detection and 

Ranging (LiDAR) or thermal imaging, and occasionally 
manual annotations of pests or disease presence [9]. 
Commonly used datasets include IP102 for pest classification, 
Sentinel-2 / Sentinel-1 remote sensing datasets for yield or 
vegetation monitoring. Evaluation metrics typically involve 
accuracy, precision, recall, F1-score, mAP (for detection 
tasks), R², RMSE, MAE (for regression tasks such as yield 
prediction), sometimes IoU for segmentation tasks, and also 
domain-specific metrics such as pesticide use reduction, crop 
loss reduction, etc [10]. Challenges across these scenarios 
include heterogeneity of data sources (different spatial, 
temporal resolutions), noisy sensor readings, missing data, 
alignment or registration issues, generalizability across 
regions/crops, and real-time computational requirements for 
deployment [11]. 

2.2 Survey of mainstream methods  
Recent years have seen a number of works aiming to fuse 

multimodal or multisource agricultural data to address some 
of these challenges. For example, Wang et al. propose a 
multimodal data fusion and embedded attention mechanism 
method for eggplant disease detection; their model integrates 
image and environmental sensor data, and achieves strong 
metrics: precision ~0.94, recall ~0.90, accuracy ~0.92, 
mAP@75 ~0.91, showing robustness under varying 
conditions [12]. Meanwhile, Jácome Galarza et al. present 
AgriTransformer, a transformer-based architecture 
combining vegetation indices (VIs) and tabular 
(weather/soil) data in crop yield estimation tasks; compared 
with linear or CNN baselines, AgriTransformer obtains R² ≈ 
0.919 vs ~0.884 for the best linear regression baseline, 
indicating substantial improvement brought by attention 
mechanisms in multimodal fusion [13]. Another example is A 
Shooting Distance Adaptive Crop Yield Estimation Method 
Based on Multi-Modal Fusion, which fuses RGB-D images with 
extracted height/depth features and additional static and 
dynamic environmental data. In their work, they achieve R² 
values around 0.94–0.95 under multiple shooting distances, 
and significantly reduced NRMSE to ~0.07–0.08, 
outperforming baselines using only RGB or single-modal data 
[14]. On the reinforcement learning side, Zhao et al. provide a 
comprehensive review of Deep Reinforcement Learning 
(DRL) applications in the intelligent transformation of 
agricultural machinery, covering path planning, navigation, 
and precision operations such as spraying. They report 
improvements in path-tracking accuracy and spray coverage, 
while also pointing out real-world challenges, including 
deployment in unstructured environments, constraints of 
sensor perception under variable conditions, and limited 
interpretability of learned policies [15]. Another domain is 
pesticide residue detection: Q. Wang et al. [16] develop sensor 
arrays fused detection methods for pesticide residues, 
combining sensor signals with data fusion to achieve faster, 
accurate detection. Each of these methods has strengths: 
using attention for better feature weighting, using static + 
dynamic data, or applying ensembles, etc. However, many 
lack adaptive optimization of pesticide application, i.e., how to 
decide when, where, and how much to spray in a dynamic 
setting, or robustness under noisy/missing sensors, or 
deployment in multi-site, multi-season real field conditions 
[17]. 

2.3 Most similar research and distinctions 
Some works are particularly close to the current study. 

For instance, integrating multi-modal remote sensing, deep 
learning, and LiDAR time-series data for plot-level maize yield 
forecasting fuses UAV-based hyperspectral/LiDAR time-



Minkuan Zhang /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 26-37 

28 

 

series with environmental features and attention-based 
fusion mechanisms to improve yield prediction performance 
across growth stages [18]. Another example is Deep Learning 
in Multimodal Fusion for Sustainable Plant Phenotyping and 
Yield Prediction, which integrates remote sensing, sensor, 
and phenotypic traits for yield estimation under variable 
climatic conditions [19]. A different work, High-Precision Pest 
Management Based on Multimodal Agricultural Perception, 
emphasizes heightened detection accuracy for pest 
infestations via multimodal fusion, though it does not 
optimize pesticide application policies [20]. Finally, Machine 
Learning-based Multimodal Data Fusion for the Prediction of 
Crop Yield under Variable Conditions explores robustness 
under variable environmental inputs [21]. These studies 
share our use of heterogeneous modalities and temporal 
dynamics, but none combine this with reinforcement learning 
to derive actionable spraying decisions in multi-season, 
multi-crop real field settings. 

2.4 Summary and gaps leading to our method 
In summary, the literature over 2023-2025 shows strong 

progress in multimodal fusion (images + sensors + 
climatic/static data), attention mechanisms, ensemble 
models, and DRL in agricultural contexts. The benefits are 
clear: better predictions, classification, detection, yield 
estimation, etc. However, the gaps remain that no existing 
method simultaneously integrates cross-modal attention 
fusion with reinforcement learning for pesticide application 
optimization, especially in fully real field or multi-season / 
multi-site settings. Robustness under noisy or missing 
sensors, temporal dynamics of pest outbreaks, and the 
decision-making aspect (amount, timing, spatial targeting of 
pesticide) are not yet sufficiently addressed [22]. These gaps 
motivate our method, which unifies heterogeneous field 
sensors, models cross-modal relationships via attention, and 
uses reinforcement learning to optimize pesticide application, 
validated over multiple real field datasets and under noise, to 
fill this lacuna and advance both academic and practical 
fronts. 

3. Methodology 

3.1 Problem formulation 
The task of pesticide application optimization in 

precision agriculture can be formally defined as a sequential 
decision-making problem grounded in heterogeneous sensor 
observations and multi-objective sustainability criteria. Let 
us assume a farming environment where a set of sensors 𝒳 =
{X(1), X(2), . . . , X(𝑀)}continuously collects multimodal data. 
Each modality mmm corresponds to a different source, such 
as hyperspectral imagery, soil nutrient probes, microclimate 
weather stations, or pest population traps. For modality 

mmm, the raw input is denoted by X(𝑚) ∈ ℝ𝑇×𝑑𝑚 , where 𝑇 is 
the temporal horizon and 𝑑𝑚 is the feature dimension. Since 
these modalities often operate at different sampling 
frequencies, temporal alignment is performed through 
interpolation and resampling, while spatial alignment may 
require image registration and sensor calibration. The 
system’s goal is to generate pesticide spraying actions. Let the 
action space be defined as 𝒜 = {𝑎𝑡 ∣ 𝑎𝑡 ∈ ℝ𝑘}, where 𝑎𝑡 
corresponds to the pesticide spraying configuration at the 
time step 𝑡. The dimensionality 𝑘 may include spray intensity, 
nozzle aperture, timing, and spatial coordinates. The state 
space 𝑆 represents the unified latent representation of multi-
modal signals, such that each state at time 𝑡 is 𝑆𝑡 ∈ ℝ𝑑 . The 
mapping from heterogeneous modalities to the state vector is 
a fusion function 𝑓fusion(·), producing 

𝑆𝑡 = 𝑓fusion(𝑋𝑡
(1)

, . . . , 𝑋𝑡
(𝑀)

)                              (1)      

A policy function parameterized by θ, denoted 𝜋𝜃(𝑎𝑡 ∣ 𝑆𝑡), 
specifies the probability distribution over actions given the 
state. The agent interacts with the farming environment by 
selecting an action, receiving feedback in the form of a 
reward, and observing a new state. The transition dynamics 
are stochastic and governed by environmental conditions 
such as pest infestation patterns, crop growth stages, and 
weather variability. The reward function is defined to balance 
three competing objectives: pest suppression effectiveness, 
reduction in chemical pesticide use, and minimization of 
ecological risk. At time t, the reward is expressed as: 

𝑅𝑡 = 𝛼 ⋅ Suppression𝑡 − 𝛽 ⋅ ChemicalUse𝑡 − 𝛿 ⋅
EcologicalRisk𝑡                 (2) 

where α, β, δ ∈ ℝ+are trade-off weights set in collaboration 
with agronomists and sustainability experts. Pest 
suppression effectiveness is measured by reductions in pest 
density per unit area, chemical usage is quantified by liters 
per hectare, and ecological risk accounts for off-target drift, 
soil residue, and biodiversity impact. 
The global objective of the optimization problem is to 
maximize the expected discounted return over a spraying 
season: 

𝐽(𝜃) = 𝔼𝜋𝜃
[∑ 𝛾𝑡𝑇

𝑡=1 𝑅𝑡]                                     (3) 

where γ ∈ (0,1] is a discount factor controlling the trade-off 
between immediate effectiveness and long-term 
sustainability. Thus, the methodology aims to learn both a 
robust cross-modal fusion mechanism that produces 
meaningful state representations and a reinforcement 
learning policy that adaptively controls spraying strategies. 

3.2 Overall framework 
The proposed framework, illustrated in Figure 1, is 

structured into three interdependent modules: the Cross-
Modal Attention Fusion, the State Representation & 
Environmental Modeling, and the Reinforcement Learning 
Decision Module. These modules form a pipeline that begins 
with raw sensor inputs and ends with optimized pesticide 
spraying strategies. In the first stage, the Cross-Modal 
Attention Fusion dynamically integrates heterogeneous 
sensor modalities, including spectral, soil, and weather 
features. Conventional concatenation or averaging 
approaches fail to capture the temporal importance of each 
modality, especially when certain signals (e.g., spectral 
indices of leaf chlorophyll) become critical under pest 
outbreak conditions, while others (e.g., soil moisture) are 
more relevant during drought stress. The attention 
mechanism allows the system to assign adaptive weights to 
each modality, thereby emphasizing informative sources and 
down-weighting noisy or less relevant data. The fused 
representation is then passed to the State Representation & 
Environmental Modeling module, which encodes the 
integrated signals into a structured state space. This module 
not only compresses the information but also simulates the 
dynamics of pest growth and environmental change through 
recurrent or temporal encoding layers. The output is 
formalized as a state vector 𝑆𝑡, a compact yet expressive 
representation ready to be consumed by the reinforcement 
learning agent. Finally, the Reinforcement Learning Decision 
Module generates spraying actions based on the encoded 
states. An actor-critic architecture is adopted, where the Critic 
evaluates value functions 𝑉(𝑆𝑡) to guide the Actor in 
generating adaptive spraying actions 𝑎𝑡. A policy gradient-



Minkuan Zhang /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 26-37 

29 

 

based algorithm enables continuous refinement of the policy 
through trial-and-error interaction, ensuring adaptability to 
shifting pest dynamics and seasonal variability. The 
framework ultimately outputs optimized spraying actions 
that reduce pesticide use while ensuring targeted application. 
It is designed to be modular and scalable: new sensor 
modalities can be added without redesigning the entire 
framework, while the RL module can be retrained to 
accommodate different crops or geographic regions. The 
overarching philosophy is to bridge advanced AI methods 
with sustainable agricultural practices, ensuring that both 
productivity and environmental protection are achieved. 

3.3 Module descriptions 
The framework consists of three modules, each 

motivated by specific limitations in existing approaches and 
designed with principled solutions. Their architectures are 
illustrated in Figures 2-4, while the computational flow is 
summarized in the pseudocode that follows. 
(1) Cross-modal attention fusion module: The motivation for 
this module lies in the heterogeneity and varying reliability of 
field sensors. Naïve concatenation often leads to modality 
imbalance, where dominant signals overwhelm subtle but 
critical cues. To address this, the module integrates spectral 

features (xs, ℎt
(s)

), soil features (xsoil, ℎt
(soil)

), and weather 

features (xw, ℎt
(w)

) through an attention mechanism. The 
principle of attention, widely used in natural language 
processing and multimodal learning, involves assigning 
dynamic weights to each input channel. Formally, given 

modality features ℎ𝑡
(𝑚)

∈ ℝ𝑑𝑚 , the attention weight for 
modality 𝑚 is computed as 

𝛼𝑡
(𝑚)

=
exp(w⊤ tanh(Wℎ𝑡

(𝑗)
))

∑ exp(w⊤ tanh(Wℎ𝑡
(𝑗)

))𝑀
𝑗=1

                                   (4) 

where W and w are trainable parameters. The fused 
representation is 

ℎ𝑡 = ∑ 𝛼𝑡
(𝑚)𝑀

𝑚=1 ⋅ ℎ𝑡
(𝑚)

                                     (5) 

This ensures that modalities most relevant to current pest 
dynamics receive higher weights, enabling context-aware 
fusion consistent with the outputs depicted in Figure 2. Figure 
2 illustrates the Cross-Modal Attention Fusion Module. 
Spectral, soil, and weather features serve as heterogeneous 
inputs, which are dynamically weighted through the attention 
mechanism based on equation (4).  

 

The resulting fused representation, formulated in equation 
(5), emphasizes modalities most relevant to current pest 
conditions, thereby producing a context-aware 
representation for downstream state modeling. 
(2) State representation and environmental modeling 
module: After fusion, the information must be temporally 
contextualized. Crops and pests evolve over time, so a purely 
static representation is insufficient. A recurrent structure, 
such as a Long Short-Term Memory (LSTM) or a temporal 
transformer encoder, is adopted to capture dependencies: 

𝑆𝑡 = 𝑓enc(ℎ1, . . . , ℎ𝑡)                                     (6) 

where fenc(·) denotes the temporal encoder. This 
representation encodes not only current sensor signals but 
also historical trends, improving predictive accuracy. As 
illustrated in Figure 3, sequential feature inputs 
(ℎ1, ℎ1, . . . , ℎ𝑡) are processed through a temporal encoder to 
produce a compact state vector 𝑆𝑡, which captures temporal 
dependencies and environmental dynamics. 
(3) Reinforcement learning decision module: The final 
module implements the policy network. A deep policy 
gradient method is used, specifically Proximal Policy 
Optimization (PPO), chosen for its stability and efficiency in 
continuous action spaces. The policy is defined as: 

𝜋𝜃(𝑎𝑡 ∣ 𝑆𝑡) = 𝒩(𝜇𝜃(𝑆𝑡), Σ𝜃(𝑆𝑡))                              (7) 

where 𝜇𝜃 and Σ𝜃 denote the mean and covariance outputs of 
the policy network, allowing stochastic exploration. The value 
function is estimated via a critic network 𝑉𝜙(𝑆𝑡). 

The overall architecture of this module is illustrated in Figure 
4. The state vector 𝑆𝑡 is simultaneously processed by the actor 
and critic networks. The actor parameterizes the Gaussian 
action distribution through 𝜇𝜃(𝑆𝑡) and Σ𝜃(𝑆𝑡), enabling 
stochastic spraying actions, while the critic estimates the 
value function 𝑉𝜙(𝑆𝑡) and provides advantageous signals for 

PPO-based updates. 

3.4 Objective function and optimization 
The optimization objective unifies cross-modal 

representation learning and reinforcement learning. The total 
loss function consists of three terms: supervised fusion loss, 
policy gradient loss, and value function loss. The weights (α, 
β, δ) were elicited through a two-round Delphi method with 
seven agronomists, followed by Analytic Hierarchy Process 
(AHP) pairwise comparisons.  

 
 

 
 

Figure 1. Overall architecture of the proposed multi-source field sensor data fusion and reinforcement learning-driven pesticide 

application optimization framework 



Minkuan Zhang /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 26-37 

30 

 

 

 
 
 

 
 

 

 

 
 
 

 
 

 

Figure 2. Architecture of the cross-modal attention fusion module 

Figure 3. Architecture of the state representation and environmental modeling module 

Figure 4. Reinforcement learning decision module 



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The final values were α = 0.46 (95% CI: 0.41–0.51), β = 

0.32 (0.28–0.36), and δ = 0.22 (0.19–0.25), with a consistency 
ratio (CR) of 0.06. Sensitivity analysis under ±20% 
perturbations showed a drop of less than 3.1% in Spray 
Precision Rate (SPR), confirming robustness. The fusion loss 
ensures alignment of modalities by minimizing discrepancy 
between predicted and ground-truth labels when available 
(e.g., pest density measurements): 

ℒfusion =
1

𝑁
∑ ∥𝑁

𝑖=1 �̂�𝑖 − 𝑦𝑖 ∥2                                   (8) 

where 𝑦𝑖  is observed pest density and �̂�𝑖  is predicted. 
The policy loss under PPO is defined as: 

ℒpolicy(𝜃) = 𝔼𝑡[min(𝑟𝑡(𝜃)𝐴𝑡 , clip(𝑟𝑡(𝜃),1 − 𝜖, 1 + 𝜖)𝐴𝑡)]    (9)  

where 𝑟𝑡(𝜃) =
𝜋𝜃(𝑎𝑡∣𝒮𝑡)

𝜋𝜃old
(𝑎𝑡∣𝒮𝑡)

, and AtA_tAt is the advantage. 

The value loss is: 

ℒvalue(𝜙) = 𝔼𝑡[(𝑅𝑡 + 𝛾𝑉𝜙(𝒮𝑡+1) − 𝑉𝜙(𝒮𝑡))2]                   (10) 

The total objective is: 

ℒtotal = 𝜆1ℒfusion + 𝜆2ℒpolicy + 𝜆3ℒvalue                      (11) 

with hyperparameters 𝜆1, 𝜆2, 𝜆3 controlling trade-offs. 
To guarantee robustness, additional regularization terms are 
introduced: 
Entropy regularization to encourage exploration: 

ℒentropy = −𝛽𝔼𝑡[𝜋𝜃(𝑎𝑡 ∣ 𝒮𝑡) log 𝜋𝜃 (𝑎𝑡 ∣ 𝒮𝑡)]                        (12) 

Modality dropout to handle missing sensors: 

ℒdropout = ∑ 𝕀𝑀
𝑚=1 [dropped(𝑚)] ∥ ℎ(𝑚) ∥2                           (13) 

Thus the final loss is: 

ℒ = ℒtotal + 𝜂ℒentropy + 𝜉ℒdropout                           (14) 

4. Experiment and Results 

4.1 Experimental setup 
To comprehensively evaluate the proposed multi-source 

field sensor fusion and reinforcement learning-driven 
pesticide application optimization model, we conducted 
experiments across multiple real-world agricultural datasets 
spanning two growing seasons. The experiments were 
designed to assess both predictive performance and decision-
making effectiveness under heterogeneous environmental 
conditions. Three components were carefully defined: dataset 
overview, hardware configuration, and evaluation metrics. 
We utilized three datasets representing distinct agricultural 
contexts: (1) a rice field dataset with hyperspectral UAV 
imagery, soil nutrient profiles, and weather station logs; (2) a 
maize dataset collected from semi-arid regions with 
multispectral imaging, pest trap counts, and soil moisture 
sensors; and (3) a soybean dataset combining canopy thermal 
imaging and environmental monitoring.  

 

 

 

 

 

 

 

Table 1 summarizes dataset characteristics, highlighting 
diversity in crop type, sensing modalities, and geographic 
regions, which ensures the evaluation covers both tropical 
and temperate farming conditions. To ensure reproducibility, 
we provide additional information on data acquisition and 
protocols. Geographic coordinates, temporal resolution, and 
sensor calibration procedures are listed in Table 2 and Table 
3. All experiments were conducted on a high-performance 
computing cluster. Table 4 details the computing resources, 
including GPU accelerators and memory capacity, which 
guarantee efficient training of deep reinforcement learning 
models and fair benchmarking across large-scale multi-
season datasets. We measured performance using both 
classification/regression metrics and domain-specific 
indices. Table 5 lists the evaluation metrics, combining 
standard prediction accuracy indicators with agricultural 
sustainability criteria, thereby providing a comprehensive 
measurement framework that jointly reflects computational 
accuracy, ecological responsibility, and pest suppression 
effectiveness. Table 6 shows the specific parameters of 
computing power and consumption. 

4.2 Baselines 
To demonstrate the superiority of our approach, we 

compared it against both classical and state-of-the-art 
baselines. Classical baselines included (1) Rule-Based 
Thresholding, where pesticide spraying was triggered when 
pest density exceeded a fixed threshold; (2) Linear 
Regression with Single Modality, using only weather data for 
spraying decisions. These represent traditional heuristics 
widely used in farm management. 
Modern machine learning baselines included: 
CNN-Spectral: Convolutional models operating only on 
hyperspectral/multispectral imagery [23]. 
RNN-Temporal: LSTM models integrating temporal pest trap 
and weather data [24]. 
MVGF (Multi-View Gated Fusion): A state-of-the-art multi-
source fusion model for crop yield prediction adapted to pest 
management [25]. 
AgriTransformer: A transformer-based multimodal attention 
model for crop yield estimation [13]. 
DRL-Spray: A deep reinforcement learning spraying model 
with single modality sensor input [15]. 
To ensure fairness, all models underwent the same Bayesian 
hyperparameter search budget (50 trials), identical early 
stopping (patience = 20), and evaluation under five random 
seeds. Search spaces and best configurations are reported in 
the supplementary material. 
These baselines represent different categories: heuristics, 
unimodal learning, fusion without Reinforcement Learning 
(RL), and RL without cross-modal fusion. Comparing it with 
them illustrates both the benefits of fusion and the 
contribution of reinforcement learning. 
 
 

(4) Pseudocode of Training Flow 

Algorithm 1. Training procedure of the reinforcement learning–driven pesticide application optimization model (PPO-based) 

Initialize parameters θ for policy network, φ for value network 
for each training episode do 
    Collect trajectories {S_t, a_t, R_t} from environment 
    Compute advantage estimates A_t = R_t + γ Vφ(S_{t+1}) – Vφ(S_t) 
    Update θ by maximizing PPO clipped objective 
    Update φ by minimizing value function error 
end for 

 



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32 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 1. Dataset overview 

Dataset Crop Type Modalities Size (Fields × Days) Label Type Geographic 
Region 

RiceSet Rice UAV hyperspectral (220 
bands), soil NPK, 

microclimate 

25 × 90 Pest density, spray 
records 

East Asia 

MaizeSet Maize Multispectral (12 bands), 
pest traps, soil moisture 

18 × 75 Pest density, 
growth rate 

North America 

SoySet Soybean Thermal canopy, weather 
logs, soil EC 

20 × 80 Infestation severity 
index 

South America 

 

Table 2. Dataset details 

Dataset Fields Days Geographic 
Range 

Spatial 
Resolution 

Temporal 
Resolution 

Modalities 

RiceSet 25 90 23.47–23.59N, 
113.15–113.30E 

10 cm Daily UAV hyperspectral (220 bands), 
soil NPK, microclimate 

MaizeSet 18 75 40.45–40.55N, -
100.15–-100.35W 

20 cm Every 2 days Multispectral (12 bands), pest 
traps, soil moisture 

SoySet 20 80 -22.75–-22.90S, -
47.10–-47.25W 

15 cm Daily Canopy thermal, weather logs, 
soil EC 

 

Table 3. Data collection protocols 

Modality Device/Spec Acquisition 
Parameters 

Calibration Method Quality Control 

Hyperspectral Headwall Nano-
Hyperspec 

120 m altitude, 80% 
overlap 

Reflectance panel Radiometric correction 

Soil NPK SoilProbe-300 0–20 cm depth sampling Lab cross-check Triplicate samples per 
plot 

Pest traps Delta pheromone traps 20 traps/ha, weekly 
inspection 

Regular replacement Manual counts cross-
verified 

Weather logs Davis Vantage Pro2 10 min logging interval Annual calibration Missing-data imputation 
strategies 

 

Table 4. Hardware configuration 

Component Specification 

CPU Intel Xeon Gold 6338 (32 cores, 2.0 GHz) 

GPU 4 × NVIDIA A100 (80 GB) 

RAM 512 GB DDR4 

Storage 20 TB SSD 

Framework PyTorch 2.1, CUDA 12.0, cuDNN 9.0 

 

Table 5. Evaluation metrics 

Category Metric Description 

Prediction accuracy RMSE, MAE, R² Assess the accuracy of pest density estimation 

SPR (Spray Precision Rate) SPR (Spray Precision Rate) Proportion of correctly targeted spraying actions 

Sustainability PUR (Pesticide Use Reduction %) Relative reduction in chemical input compared to baseline 

Control effectiveness SER (Suppression Effectiveness Rate) Reduction in pest population post-application 

Robustness Performance Degradation Rate Drop in SPR under noisy/missing sensors 

 

Table 6. Compute the budget and energy consumption 

Model GPUs Used Training Time 

(h) 

Average Power 

(W) 

Energy (kWh) Estimated CO₂e 

(kg) 

Proposed 4 × A100 72 1,200 345.6 148.3 

AgriTransformer 2 × V100 46 650 74.8 32.1 

DRL-Spray 2 × A100 58 1,000 116.0 49.8 

 



Minkuan Zhang /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 26-37 

33 

 

4.3 Quantitative results 
Table 7 presents the quantitative comparison across 

datasets. Our model consistently outperformed baselines in 
precision, reduction of pesticide use, and suppression 
effectiveness. The results clearly show that the proposed 
approach achieves the lowest RMSE and highest R², while also 
delivering substantial improvements in spray precision, 
sustainability, and pest suppression effectiveness across all 
benchmarks. Statistical tests confirmed significance. A paired 
t-test between our model and AgriTransformer yielde p<0.01 
across SPR and SER, indicating robust improvements. Figure 
5 illustrates the convergence of training rewards, where the 
proposed model reaches stability faster and with smaller 
fluctuations than DRL-Spray, highlighting the effectiveness of 
cross-modal attention in accelerating learning. 

Table 7. Quantitative comparison (Mean ± SD) 

Model RMSE ↓ R² ↑ SPR ↑ PUR 

(%) ↑ 

SER 

(%) ↑ 

Rule-Based 0.412 

± 0.05 

0.62 61.3 0 45.8 

Linear 

Regression 

0.389 

± 0.04 

0.65 63.7 2.5 47.6 

CNN-Spectral 0.271 
± 0.03 

0.78 71.5 8.6 56.2 

RNN-Temporal 0.254 
± 0.02 

0.80 74.2 10.1 57.9 

MVGF 0.219 

± 0.02 

0.85 78.9 13.2 61.7 

AgriTransformer 0.205 

± 0.01 

0.87 80.6 14.1 63.0 

DRL-Spray 0.197 

± 0.02 

0.88 82.1 15.9 65.4 

Proposed Model 0.162 

± 0.01 

0.92 88.3 18.3 77.9 

 

The curve illustrated in Figure 5 shows faster and more 
stable convergence of our method compared with DRL-Spray, 
demonstrating the efficiency of cross-modal fusion. 

4.4 Qualitative results 
We further analyzed field-level case studies. Figure 6 

demonstrates spraying map visualizations across different 
methods. The proposed model achieves precise targeting that 
closely matches actual infestation regions, minimizing 
unnecessary chemical application. By contrast, the rule-based 
approach results in excessive coverage, while CNN-Spectral 
exhibits incomplete spraying, highlighting the superiority of 
cross-modal attention with reinforcement learning. 

 

 
Figure 5. Convergence curve of training reward 

Building upon these spraying visualizations, Figure 7 
focuses on hotspot detection accuracy, comparing infestation 
heatmaps across models. Together, Figures 6 and Figure 7 
illustrate how perception quality directly influences spraying 
decisions, thereby reinforcing the tight coupling between 
sensing and action in our framework. Finally, Figure 8 
contextualizes these findings in an applied field scenario, 
showing how UAV spraying and ground sensors operate in 
combination to achieve precise crop protection. The 
proposed model achieves the closest alignment with 
agronomist-annotated ground truth, producing the lowest 
mean error. By contrast, CNN-Spectral underestimates 
hotspots and RNN-Temporal yields false positives, 
highlighting the advantage of cross-modal attention in 
reducing ambiguity. To validate interpretability, attention 
heatmaps were shared with agronomists. They confirmed 
that high-weight signals (e.g., hyperspectral indices during 
pest outbreaks, weather features during drought) aligned 
with field observations. Figure 8 shows an example where 
attention patterns matched infestation hotspots, 
demonstrating that the model’s decisions can be practically 
interpreted and acted upon by farmers. A simulated 
illustration depicts the UAV-mounted spraying experiment in 
rice fields, showing drones releasing pesticide mist while 
ground sensors monitor soil moisture and microclimate 
conditions. This experimental setup visualization (Figure 8) 
highlights how aerial and terrestrial sensing devices are 
integrated to support precise spraying decisions, 
underscoring the model’s real-world applicability. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Figure 6. Comparison of spraying maps generated by different methods 



Minkuan Zhang /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 26-37 

34 

 

 
Figure 7. Infestation heatmap comparison 

 

 
Figure 8. Real field scene (simulated illustration) 

 
 

4.5 Robustness 
Robustness experiments evaluated three aspects: (1) 

multi-task generalization across different crops, (2) resilience 
to sensor noise, and (3) adaptation to missing modalities. 
Gaussian noise with standard deviations up to 20% was 
injected into sensor readings. Figure 9 plots SPR versus noise 
level. Our method degrades gracefully (<5% drop at 20% 
noise), outperforming MVGF and DRL-Spray, which degrade 
by >12%. Training on RiceSet and testing on SoySet, the 
model maintained 82.5% SPR, whereas AgriTransformer 
dropped to 75.4%. This suggests strong domain transfer 
capability. When hyperspectral imagery was removed, our 
method still achieved 83.6% SPR by leveraging soil and 
weather data, thanks to modality dropout regularization. 

4.6 Ablation study 
To verify the contributions of each module, we 

conducted ablation experiments. Table 8 highlights the 
importance of each module in the proposed framework. 
Cross-modal attention yields substantial gains by effectively 

 

 

integrating heterogeneous inputs, while temporal encoding 
enhances dynamic adaptation. The RL optimization 
component drives the largest improvements in SPR and SER, 
and robustness regularization ensures reliable performance 
under sensor perturbations, confirming the necessity of the 
complete design. The removal of cross-modal attention 
caused a 5.7% drop in SPR, demonstrating its necessity. RL 
optimization contributed the largest improvement, 
confirming the role of adaptive control. Regularization was 
also critical for maintaining performance under noisy 
conditions. 

4.7 Summary of results 
The experimental evaluation demonstrates that the 

proposed framework consistently delivers superior 
performance across predictive, decision-making, and 
sustainability metrics. Quantitative analyses confirm 
significant improvements over both heuristic and advanced 
baselines, with stable convergence and strong statistical 
significance. Compared with AgriTransformer, our model’s 
gain in Spray Precision Rate (+14.7%) was statistically 
significant (paired t-test, p < 0.01; bootstrap 95% CI: +11.9% 
to +17.3%). ANOVA with Tukey post-hoc confirmed 
consistent superiority across datasets. Qualitative 
visualizations further highlight its ability to localize pest 
hotspots and minimize unnecessary spraying precisely. 
Robustness studies show resilience to noisy and missing 
inputs, while ablation experiments validate the necessity of 
each module, particularly reinforcement learning 
optimization and cross-modal attention. Together, these 
results establish a clear empirical foundation for the 
framework, confirming that integrating heterogeneous 
sensing, temporal encoding, and adaptive decision-making 
yields measurable benefits in real-world crop protection 
scenarios. 

 

 

 



Minkuan Zhang /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 26-37 

35 

 

Figure 9. Robustness evaluation across noise and missing modalities 

Table 8. Ablation study results 

Configuration SPR ↑ PUR (%) ↑ SER (%) ↑ 

Full Model 88.3 18.3 77.9 

– w/o Cross-Modal 

Attention 

82.6 13.7 70.1 

– w/o Temporal 

Encoding 

80.4 12.5 68.9 

– w/o RL Optimization 

(Greedy) 

76.3 9.8 64.2 

– w/o Robustness 
Regularization 

84.1 14.2 72.0 

 

5. Discussion 

The experimental results demonstrate that the proposed 
cross-modal attention and reinforcement learning 
framework consistently outperforms both classical heuristics 
and state-of-the-art models in pesticide application 
optimization. The superior performance—achieving 88.3% 
spray precision, 18.3% pesticide use reduction, and 77.9% 
pest suppression effectiveness—can be attributed to two key 
design choices. First, the cross-modal attention mechanism 
with modality dropout ensures that the system dynamically 
prioritizes the most informative signals under varying field 
conditions (e.g., spectral indices during pest outbreaks, soil 
parameters under drought stress), while maintaining 
robustness when inputs are noisy or missing. Second, the 
reinforcement learning decision module adapts spraying 
actions to temporal fluctuations in pest dynamics and 
environmental factors, surpassing static thresholding or 
unimodal predictors. Compared with AgriTransformer, which 
focuses on multimodal perception but lacks a closed-loop 
decision layer, our framework explicitly integrates sensing 
and action, thereby bridging prediction with actionable 
control. Relative to DRL-Spray, which applies reinforcement 
learning to a single modality, our model leverages 
heterogeneous inputs to improve generalizability across 
crops and sites. Importantly, these gains were achieved under 
a fair evaluation protocol, with equal hyperparameter search 
budgets and multiple random seeds, and were confirmed by 
statistical tests (paired t-tests and ANOVA, p < 0.01). The 
reward function design also contributed to model 
effectiveness. Unlike arbitrary parameterization, trade-off 
weights (α, β, δ) were elicited through structured expert input 
(Delphi + AHP with seven agronomists) and validated via 
sensitivity analysis.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
Results show that system performance remains stable 

under ±20% weight perturbations, underscoring the 
robustness of the reward formulation and ensuring that 
agronomic expertise is faithfully reflected in optimization 
objectives. Beyond numerical metrics, interpretability is a 
crucial feature for adoption. Attention heatmaps presented to 
agronomists revealed that the model’s prioritization of 
modalities corresponded to real pest and environmental 
conditions. Experts confirmed that high-attention intervals 
aligned with infestation hotspots or microclimatic anomalies, 
demonstrating that model outputs can be translated into 
farmer-actionable strategies rather than remaining opaque 
“black-box” predictions. 

Nevertheless, the reinforcement learning module 
requires substantial computational resources. Training our 
PPO-based model demanded four NVIDIA A100 GPUs for 
approximately 72 hours, with an estimated energy 
consumption of 345.6 kWh (148.3 kgCO₂e). While feasible for 
research settings, deployment in resource-constrained farms 
may require lightweight versions of the model or edge-
optimized implementations. Reporting such compute budgets 
is essential for assessing the real-world feasibility and 
sustainability of AI solutions. Generalizability remains a 
central challenge. Although validated on rice, maize, and 
soybean datasets across three continents, broader testing is 
required under different climates, pest species, and farming 
practices. Future research should explore domain adaptation 
strategies (e.g., conditional normalization with local climate 
indices, lightweight fine-tuning) to extend applicability. 
Moreover, sustainability goes beyond reducing pesticide 
volume. Long-term ecological considerations include (i) 
pesticide resistance evolution, which could be modeled as a 
cumulative penalty for repeated chemical applications; (ii) 
non-target insect impacts, particularly on pollinators, which 
could be integrated into the reward as ecological risk proxies; 
and (iii) spray drift monitoring, supported by UAV flight 
constraints and field-side trap validation. 

In summary, the proposed framework demonstrates 
how combining context-aware sensor fusion with adaptive 
reinforcement learning control can advance both academic 
research and agricultural practice. By providing 
interpretable, statistically validated, and ecologically 
grounded improvements, this work represents a meaningful 
step toward intelligent, sustainable, and field-ready crop 
protection systems. 

 



Minkuan Zhang /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 26-37 

36 

 

6. Conclusion 

This study proposed a multi-source field sensor data 
fusion and reinforcement learning-driven optimization 
framework for sustainable pesticide application. By 
introducing a cross-modal attention mechanism to adaptively 
integrate heterogeneous sensor signals, a temporal encoder 
to capture environmental dynamics, and a PPO-based 
decision module for adaptive spraying, the model addressed 
critical limitations of existing approaches. Experimental 
results across three crop datasets demonstrated significant 
improvements in predictive accuracy, spray precision, 
pesticide reduction, and pest suppression effectiveness, with 
robustness maintained under noisy and incomplete sensing 
conditions. The contributions of this work extend beyond 
technical performance gains. From an academic perspective, 
the integration of cross-modal attention and reinforcement 
learning provides a principled methodology for unifying 
multimodal perception with adaptive decision-making in 
precision agriculture. From a practical standpoint, the 
framework offers a pathway toward reducing chemical 
inputs, mitigating ecological risks, and improving food 
production sustainability through UAV-based or automated 
spraying systems. In future developments, the proposed 
model can be extended to other agricultural tasks such as 
irrigation, fertilization, and disease monitoring. Further 
research may also focus on lightweight deployment on edge 
devices, integration with causal inference for interpretability, 
and multi-agent reinforcement learning for coordinated 
operations. Collectively, these directions hold promise for 
advancing intelligent, sustainable, and autonomous crop 
protection. 

Ethical issue 
The author is aware of and complies with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 
be available upon request from the author. 

Conflict of interest 
The author declares no potential conflict of interest. 

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