FLPNet: A Lightweight Real-Time Apple Leaf Disease Detection Model Based on Frequency-Spatial Dual-Domain Decoupling

doi:10.12133/j.smartag.SA202605024

Abstract

Abstract:

[Objective] In complex imaging environments, dynamic lighting variations and foliage occlusion can degrade the visibility of disease spot features, making the accurate identification of early-stage micro-lesions difficult for existing models. To address the challenges associated with apple leaf disease detection in natural environments characterized by significant lighting interference, an enhanced real-time detection transformer (RT-DETR) model, termed the frequency-lighting-aware perception network (FLPNet), is proposed for disease detection under complex imaging conditions. [Methods] FLPNet was developed to overcome the limitations of traditional convolutional neural networks in spatial-domain feature extraction by incorporating a frequency-domain decoupling and enhancement module (FDEM). By leveraging the complementary properties of wavelet transform and Fourier analysis, FDEM effectively decoupled and reconstructed fine-grained texture features of occluded disease spots. Furthermore, a physics-driven Retinex layer, designed as a feature enhancement module based on Retinex theory, was introduced to mitigate the effects of uncontrolled illumination. A low-resolution global attention module was also incorporated to reduce computational redundancy while preserving global contextual information. [Results and Discussions] FLPNet achieved an mAP@0.50 of 85.7%, representing an improvement of 5.0 percentage points, while reducing the number of parameters by 3.3 M and the computational cost by 9.1 GFLOPs. Compared with YOLOv12n, FLPNet achieved a 4.1 percentage points improvement in mAP@0.75. The results showed that FLPNet outperformed state-of-the-art models, including YOLOv12n and DEIM, in overall detection performance. The proposed modules significantly reduced computational complexity while enhancing disease detection capability under complex imaging conditions. [Conclusions] FLPNet improves apple leaf disease detection under complex natural imaging conditions while substantially reducing computational complexity. The model achieves a favorable balance between detection performance and computational efficiency, indicating potential for deployment on resource-constrained agricultural edge devices. These characteristics provide essential theoretical support and technical guidance for the practical application of apple leaf disease detection.

Key words: apple leaf diseases, RT-DETR, attention mechanism, Retinex theory, lightweight

CLC Number:

TP183

SONG Xiangshuo, WANG Jun. FLPNet: A Lightweight Real-Time Apple Leaf Disease Detection Model Based on Frequency-Spatial Dual-Domain Decoupling[J]. Smart Agriculture, 2026, 8(5): 219-237.

Figures/Tables 21

Table 1

Description of common apple leaf diseases

Disease type	Scientific name	Symptoms	Period of high incidence
Alternaria leaf spot^[15]	Alternaria spp	Dark brown spots appear on the leaves, evolving into circular or irregular lesions characterized by grayish-brown centers and occasionally concentric rings. The necrotic tissue leads to premature wilting and dropping of the leaves	It mainly occurs between 70 and 110 days after flowering, with infection typically starting around 20 days after flowering
Brown spot^[16]	Alternaria alternata f. sp. mali	Dark brown spots expand into larger lesions, accompanied by a rapid yellowing of the surrounding tissue, forming a distinct "halo". This process leads to extensive premature leaf defoliation	From summer to early September, conditions favoring early defoliation disease are prevalent due to the hot and rainy climate. The peak period for this disease extends until early September
Frogeye leaf spot^[17]	Botryosphaeria dothidea	Purple-brown specks develop into circular spots characterized by light centers and dark margins, resembling a "frog's eye". The necrotic center may detach, potentially leading to leaf drop	Its peak period usually occurs from late spring to early summer
Mosaic^[18]	Apple mosaic virus	Light and dark green mosaic patterns appear on leaves, with yellow patches reducing chlorophyll. This impairs photosynthesis and weakens the tree	Symptoms can occur throughout the year, but are often more pronounced during the spring shoot growth period
Powdery mildew^[19]	Podosphaera leucotricha	White powdery patches form on the leaves, causing them to curl and distort. The affected areas become stunted, potentially leading to premature leaf loss	Spring is the peak season for the occurrence of diseases; however, the severity of these diseases varies across different regions. In some areas, diseases may persist throughout other periods of the growing season
Rust^[20]	Gymnosporangium yamadae	Yellow-orange spots appear on the leaf surface, accompanied by corresponding cup-shaped structures forming beneath. The leaves may curl, desiccate, and abscise prematurely	Spring and early summer represent the peak periods for rust disease, particularly in the Shaanxi production area, where a high incidence of rust primarily occurs during the early to middle stages of the growing season
Scab^[21]	Venturia inaequalis	Velvety, olive-green to black spots develop on the leaves, evolving into raised, scabby lesions. The surrounding tissue may exhibit yellowing, which can lead to curling and premature leaf drop	Spring is a critical period for the occurrence of apple scab, as it is closely linked to the rainfall and humidity conditions prevalent during this season

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Category	Total images	Training/images	Validation/images	Test/images
Alternaria leaf spot	812	568	82	162
Brown spot	790	553	79	158
Frogeye leaf spot	805	564	80	161
Healthy leaf	795	556	80	159
Mosaic	788	552	78	158
Powdery mildew	815	570	82	163
Rust	800	560	80	160
Scab	804	562	81	161

Backbone	P/%	R/%	mAP@0.50/%	mAP@0.75/%	mAP@0.50∶0.95/%	F ₁/%	FLOPs/G	Parameters/M	Model file size/M	FPS
RT-DETR-r18	87.6	78.8	80.7	49.2	49.5	82.9	57.0	19.8	77.0	112.4
MambaYolo	87.2	78.8	82.6	43.6	45.3	82.5	17.2	9.1	34.9	185.2
Mamba	89.6	82.2	84.7	48.2	49.3	85.6	54.5	19.2	74.5	108.7
LWGA	89.4	81.5	84.2	47.4	48.5	85.1	44.7	13.3	52.1	129.9
CNCM	88.2	82.2	84.8	48.0	48.3	85.0	64.5	19.7	76.6	99.0
EVA	88.3	82.1	83.9	47.5	48.8	84.9	48.8	14.5	56.7	123.5
FDEM	89.8	82.4	84.5	50.1	51.0	85.9	47.5	16.4	73.3	120.5

AIFI	P/%	R/%	mAP@0.50/%	mAP@0.75/%	mAP@0.50∶0.95/%	F ₁/%	FLOPs/G	Parameters/M	Model file size/M	FPS
SWSA	84.8	77.5	79.0	46.4	46.9	81.0	55.6	18.3	71.2	115.6
DWMMSA	85.4	78.3	79.8	47.2	47.7	81.7	56.1	18.9	73.5	114.2
BinaryAttn	86.0	79.1	80.4	48.1	48.5	82.4	56.7	19.2	75.1	113.1
WCA	86.9	80.1	81.1	49.1	49.3	83.4	57.0	19.6	76.4	112.4
LRGA	87.9	80.8	81.5	49.4	49.8	84.2	57.2	19.9	77.3	111.8

RepC3	P/%	R/%	mAP@0.50/%	mAP@0.75/%	mAP@0.50∶0.95/%	F ₁/%	FLOPs/G	Parameters/M	Model file size/M	FPS
Converse2DC3^［31］	88.1	79.8	82.7	44.6	46.3	83.7	48.1	18.1	69.5	127.6
MBRConv5C3^［32］	87.8	80.4	82.3	45.8	47.1	83.9	52.4	19.1	73.3	119.4
GCConvC3^［33］	88.2	81.7	82.9	46.7	48.5	84.8	57.0	19.9	76.4	112.3
PDRL	88.1	81.1	81.8	49.6	49.9	84.4	56.7	19.8	76.0	113.0

Model	P/%	R/%	mAP@0.50/%	mAP@0.75/%	mAP@0.50∶0.95/%	F ₁/%	FLOPs/G	Parameters/M	Model file size/M	FPS
RT-DETR-r18	87.6	78.8	80.7	49.2	49.5	82.9	57.0	19.8	77.0	112.4
Faster-RCNN^［34］	81.3	73.7	75.5	44.0	43.5	77.3	370.2	137.1	108.0	28.6
SSD^［35］	87.1	60.0	74.0	44.3	43.9	71.1	62.7	26.3	94.1	97.8
YOLOv5s	86.3	77.6	82.0	49.6	47.8	81.7	16.0	7.2	29.0	219.4
YOLOv8s	86.5	79.0	81.9	49.1	49.4	82.6	28.6	11.2	44.8	176.8
YOLOv9t^［36］	86.9	78.4	82.6	50.3	50.1	82.4	7.7	2.0	8.0	275.6
YOLOv10n^［37］	85.6	76.6	81.4	49.5	50.0	80.8	6.7	2.3	9.2	292.4
YOLOv11n^［38］	86.8	77.5	82.7	50.0	50.3	81.9	6.3	2.6	10.4	301.2
YOLOv12n^［39］	86.1	75.9	80.8	48.0	49.3	80.7	6.5	2.6	10.4	296.7
RT-DETR-r34	89.0	82.1	84.9	51.4	51.3	85.4	95.2	31.8	123.6	73.8
DETRv2-r18	88.7	81.7	84.7	50.9	50.8	85.1	58.2	20.1	78.1	109.8
D-FINE-N	87.2	78.6	83.0	50.5	50.3	82.7	8.4	4.2	16.4	252.7
D-FINE-S	88.5	80.7	84.5	51.5	51.3	84.4	24.6	10.7	42.2	161.3
DEIM^［40］	88.7	78.7	81.9	49.7	49.5	83.4	25.0	10.0	41.2	158.4
DEIMv2^［41］	88.3	79.8	83.1	50.2	49.8	83.8	25.6	9.7	39.8	162.5
FLPNet	89.6	82.7	85.7	52.1	52.0	86.0	47.9	16.5	74.0	119.6