Fruit Disease Detection Using Lightweight Transfer Learning Techniques

Anuradha S. Varal

1,2 a

and Shabnam F. Sayyad

1 b

AISSMS College of Engineering, Pune, India

AISSMS Institute of Information Technology, Pune, India

Keywords:

Fruit Disease Detection, Lightweight Models, Transfer Learning, Data Augmentation.

Abstract:

Fruit disease identiﬁcation is crucial and must be performed quickly to enhance the productivity of agriculture

and reduce crop losses. In this context, fruit disease classiﬁcation with CNN, powered by efﬁcient transfer

learning, is proposed. The pre-trained weights for both MobileNetV2 and VGG16 models are used; some

of the initial layers are selectively frozen to create a trade-off between model performance and computational

efﬁciency. This approach will allow us to retain critical features learned from large-scale datasets with reduced

training loads on limited hardware. By optimizing the model, high classiﬁcation accuracy can be achieved with

a reduction in processing time and lower RAM consumption, which eventually will make the approach most

suitable for deployment on devices with limited resources. To develop variability within the dataset and limit

overﬁtting, augmentations like rotation, ﬂipping, and zooming would be performed on the augmented data.

Experimental sessions were carried out on a publicly available dataset of fruit disease images from several

classes, showing healthy and diseased conditions. The results clearly describe how, among all, MobileNetV2

ensures the best trade-off between accuracy and efﬁciency for such applications in real time. Overall, this

work showed a proper approach on how to conduct the detection of fruit diseases with lightweight transfer

learning models and provided useful insights into implementing the technology for precision agriculture in

resource-constrained settings.

1 INTRODUCTION

Due to the increase in the global population, de-

mand for agricultural products has increased ever

more and caused agriculture to bear a greater bur-

den toward supporting sustainable development and

ensuring food security around the world (Wang and

Su, 2022). Agriculture is not only about economic

stability, but it also plays an important role in being

one of the major sources of food, income, and em-

ployment in most parts of the world (Eunice and He-

manth, 2022). Where the possibilities of expansion of

agricultural land are limited, erosion of agricultural

productivity has become the only feasible manner of

meeting the demand. The more critical issues facing

these productivity increases are the losses incurred by

fruits due to the attack of diseases that always seri-

ously affect its yield and quality (Dhaka, 2021). The

early detection and treatment of diseases in fruits are

very vital in reducing losses since they will prevent

further results of infection and limit damage. Con-

https://orcid.org/0000-0003-4435-4591

https://orcid.org/0000-0001-8167-8451

ventional methods of fruit disease detection and iden-

tiﬁcation rely on expert observation by the naked eye.

Though it is expected, most developing regions usu-

ally have experts in far-ﬂung locations, and such ex-

pertise is costly in terms of time. Therefore, auto-

mated fruit disease detection has become very crit-

ical; it enables the early detection of the symptoms

of a disease the moment they appear on the growing

fruits. It indeed improves response times and provides

expert-level analysis access and scalability, especially

in resource-constrained settings.

Figure 1: Healthy and infected Fruit images collected from

dataset

Early detection of diseases in fruits and vegetables

has been enabled lately by several machine learning,

deep learning, and IoT-based approaches. Since agri-

culture today is intended towards its sustainable de-

498

Varal, A. S. and Sayyad, S.

Fruit Disease Detection Using Lightweight Transfer Learning Techniques.

DOI: 10.5220/0013595600004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 2, pages 498-505

ISBN: 978-989-758-763-4

velopment, early disease identiﬁcation should neces-

sarily be intelligent technologically. Advanced imag-

ing techniques and machine learning models are con-

sidered good data-driven frameworks in which a large

number of variables have complex relations. In re-

cent times, among them, much attention has been re-

ceived by deep learning methods, mainly Convolu-

tional Neural Networks and their variants for differ-

ent complex agricultural challenges due to the fact

that from images contextual information will be ex-

tracted with global descriptors, hence reduced miss-

ing values, minimized errors, and better characteri-

zation than any conventional method. Deep Trans-

fer Learning has given disease detection a further

boost by making possible tuned models for domain-

speciﬁc datasets (Hasan, 2023). Also, sensor data

generated and transmitted over IoT networks support

remote monitoring of crops from agrarians. Cyber-

agriculture methods, in turn, give a technological

backbone for large-scale deployment. Integrating

deep-learning algorithms has been a promising ap-

proach toward early detection of fruit diseases by

making use of high-resolution imagery and automated

analysis (Quy V.K., 2022). Thus, a review of the re-

cent advances and challenges would help to collate

the knowledge in detecting fruit and vegetable dis-

eases. The study at hand considers these challenges

by examining the potential of three lightweight trans-

fer learning models, MobileNetV2, InceptionV3, and

VGG16, for fruit disease detection.

The proposed system relies on two high-

performance yet computationally efﬁcient models,

optimized by pre-trained weights and selective layer

freezing to reach an effective classiﬁcation with low

consumption of memory and time. Further, these

are used with data augmentation to give more robust-

ness across a wide range of environmental conditions.

Contribution towards precision agriculture: Incorpo-

rate scalability for disease detection for the sole aim

of maximizing output to support sustainable agricul-

tural practices in resource-constrained environmental

setups. The contributions of this paper are:

• To Evaluate Lightweight Transfer Learning Mod-

els: Assess the effectiveness of MobileNetV2, In-

ceptionV3, and VGG16 for fruit disease detec-

tion, focusing on their ability to achieve high ac-

curacy with low computational demands.

• To Optimize Model Performance: Implement pre-

trained weights and selective layer freezing to en-

hance the performance of the models, aiming to

reduce training time and memory usage.

• To Implement Data Augmentation Strategies: En-

hance model robustness and generalization by ap-

plying data augmentation techniques, ensuring re-

liable disease detection across diverse environ-

mental conditions.

2 LITERATURE REVIEW

Ramazan Hadipour-Rokni et al.(Ramazan

Hadipour-Rokni, 2023) studied CNNs for identifying

the type and stage of citrus fruit diseases due to a

certain type of pest infestation. Images of citrus fruits

were captured before the infestation, at the beginning

of the infection, and eight days after infection,

amounting to 1,519 images in the dataset taken under

highly illuminated natural conditions. The methods

used pre-trained CNN models. The VGG-16 model

optimized by SGDm showed the best performance in

the accuracy of pest detection.

Gulzar Y et al.(Gulzar, 2023) proposed a modi-

ﬁed architecture by adding a ﬁve-layer head to Mo-

bileNetV2, comprising special architecture. They de-

veloped the TL-MobileNetV2 model by reusing the

pre-trained weights of MobileNetV2 and thus had a

very impressive performance with an accuracy of 99%.

Their work has pointed out that transfer learning and

dropout strategies were quite necessary in enhancing

the results and reducing overﬁtting for the fruit clas-

siﬁcation task.

Zia Ur Rehman et al.(Zia Ur Rehman, 2022) pre-

sented several high-resolution images from different

diseases of leaves of citrus from various agricultural

settings and used some pre-trained CNN architectures

such as VGG16, ResNet50, and InceptionV3. Elab-

orate pre-processing methodologies, such as scaling,

normalization, and increasing the data by augmenta-

tion, were used to make the model robust.

Ashok Kumar Saini et al.(Ashok Kumar Saini and

Srivastava, 2022) explored transfer learning to iden-

tify and classify various diseases affecting the citrus

variety of fruits, by employing a number of different

pre-trained deep learning models. Their approach re-

duced the data and computational resource require-

ments to almost negligible while training on a rich

dataset of images related to citrus fruits. These re-

sults conﬁrm that these sophisticated methods help in

disease control and, in turn, will help to keep crops

healthy and improve the yield. This underlines the

importance of the integration of AI techniques in agri-

culture regarding the successful identiﬁcation of the

disease at a faster pace.

W. G

omez-Flores et al.(W. G

omez-Flores and

Varela-Fuente, 2022) compared some CNN archi-

tectures, like ResNet18, GoogLeNet, Inception-V3,

AlexNet, VGG16, and VGG19, for the detection of

Huanglongbing disease and other disorders in Cit-

Fruit Disease Detection Using Lightweight Transfer Learning Techniques

499

rus sinensis leaves. Results showed that their mod-

els were heavily reliant on the number of trainable

parameters needed for HLB detection, even with a

deeper architecture than VGG19 could achieve per-

fect sensitivity compared to Inception-V3.

Brown D et al.(Brown D, 2021) performed trans-

fer learning with the MobileNetV2 model to identify

fruit diseases, taking its inverted residuals and linear

bottlenecks to perform effective feature extraction.

Therefore, their work proved that MobileNetV2 could

effectively recognize fruit diseases from captured im-

ages of natural agricultural environments with high

accuracy and recorded an accuracy of 92%on a cus-

tom dataset of apple orchards.

Wang Y et al.(Wang, 2021) proposed, an on-

ﬁeld fruit disease diagnostics framework using a Mo-

bileNet architecture. Because of its lightweight na-

ture, it is well suited for on-the-spot real-time test-

ing on a mobile platform, with an accuracy of up

to 92% and a unique Apple orchard dataset. This

demonstrates the practical applicability of MobileNet

in agriculture for farmers by providing an inexpensive

way toward low-power modern devices.

Lopez R et al.(Lopez, 2021) explored the Xcep-

tion model for diagnosing fruit diseases through trans-

fer learning using the Fruit360 dataset and obtained

accuracy as high as 90%. The efﬁciency of depthwise

separable convolutions of the model makes it effec-

tive for classifying images on a large scale, which is

very intentional in agricultural applications that aim

to yield a high degree of accuracy with limited com-

putational effort.

The ResNeXt model was proposed by Zhang Y

et al. (Zhang, 2021), performing fruit disease de-

tection in an apple orchard using transfer learning.

The ResNeXt model was trained using a proprietary

dataset and gave an accuracy of 92%, along with good

robustness against changes in the orchard conditions.

Its modular architecture is enhancing its generaliza-

tion and feature extraction, thereby improving disease

control practices.

Park J et al.(Park, 2021), in the year 2021, pro-

posed an SE-ResNet model which could manage

an accuracy of 95% while considering the Fruit360

dataset. The incorporation of squeeze-and-excitation

blocks in this model allowed the model to capture

more channel dependencies and thus always upgraded

its performance on fruit disease recognition. This

supports the relevance of the SE-ResNet model for

reliable identiﬁcation of diseases.

Lee H et al.(Lee, 2020) conducted research into

the use of the ResNet50 model for disease diagnosis

in fruits, which ultimately demonstrated an efﬁcient

detection performance using deep residual learning

for feature extraction. Because of these deep archi-

tectures and skip connections, ResNet50 is able to

achieve 94% accuracy on the PlantVillage dataset and

proves to be a reliable option for timely disease diag-

nosis. It also points out that, in general, the most im-

portant role of transfer learning is to reduce the train-

ing time by improving model performance.

Gupta R et al.(Gupta, 2020) (2020) studied trans-

fer learning with the NASNet model for the identiﬁca-

tion of fruit diseases. The NASNet model achieved an

accuracy of 96% on PlantVillage by structuring itself

to adapt exactly to the peculiarities of the dataset. It

is evident from this work that advanced neural archi-

tecture search signiﬁcantly improves the performance

and efﬁciency of NASNet and hence is viable for use

even on challenges of image classiﬁcation problems

that improve agriculture-based disease management.

Recently, Y. Nagaraju et al.(Y. Nagaraju and

Stalin, 2020) applied the optimized VGG-16 network

in 2020 for the classiﬁcation of eight types of ap-

ple and grape leaf diseases. Fine-tuning a new out-

put layer of the model, while retaining original layers

from VGG-16, reduced training parameters by 98.9%.

Pan F et al.(YPan F, 2023) created a lightweight

channel authentication technique utilizing frequency-

domain feature extraction in order to differentiate

between authorized and unauthorized transmitters in

agricultural wireless networks, A dataset of common

smart agriculture scenarios with both indoor and out-

door communication channels was gathered for the

study. When compared to existing ViT models, their

modiﬁed FDFE-MobileViT model showed better con-

vergence speed, accuracy, and performance.

Yan Zhang et al.(Yan Zhang, 2024) introduced

TinySegformer, model for edge computing and agri-

cultural pest identiﬁcation. TinySegformer achieves

great precision and accuracy in semantic segmenta-

tion tasks by combining Transformers with neural

networks. The lightweight design of the model, which

uses quantization and sparse attention methods, ﬁts

the processing and storage constraints of edge de-

vices. TinySegformer beats well-known models like

DeepLab, SegNet, and UNet when tested on both

public and self-gathered datasets.

Siyu Quan et al.(Siyu Quan, 2024) presented a

dataset of crop diseases derived from actual ﬁeld situ-

ations in order to train and validate models and im-

prove generality in crop disease detection research.

Through the use of partial and point-wise convolu-

tions in place of conventional deep convolution, the

model preserves performance while lowering compu-

tational complexity.

Sahil Verma et al. (Sahil Verma, 2023) pre-

sented a lightweight convolutional neural network

INCOFT 2025 - International Conference on Futuristic Technology

500

to detect illnesses in wheat, rice, and corn. The

model achieves higher accuracy than models such as

VGG16, VGG19, ResNet50, MobileNetV2, and oth-

ers by utilizing convolution layers of different sizes to

detect infections across different locations.

3 PROPOSED METHODOLOGY

Proposed methodology initiates with the very pre-

liminary collection of a dataset comprising images

of healthy and diseased fruits. Pre-processing in-

volves resizing all images to a constant dimension,

normalizing to keep the pixel value range consistent

throughout the image, and converting color space of

images if required. Further, pre-processing is fol-

lowed by the use of data augmentation techniques

that include rotations, ﬂips, and zooms in order to

increase variety in the dataset for better feature ex-

traction. The dataset is then split into a train-test

split. Later, lightweight models such as MobileNetV2

and VGG16 were used with transfer learning by using

their pre-trained weights. The models will be strong

in classifying fruit diseases while optimizing for efﬁ-

ciency and resource constraints. Figure 2: Proposed

system architecture.

Figure 2: Proposed Fruit Disease Detection Model Flow

Detailed description of each phase for fruit disease

classiﬁcation:

• Data Collection: Any machine learning model

ﬁrst needs the collection of a dataset containing

a very diverse number of images of both healthy

and diseased fruits. Publicly available datasets in-

clude the Fruit Disease dataset.

• Pre-processing: In this phase, the collected im-

ages undergo several pre-processing steps to pre-

pare them for effective model training:

-Image Resizing: Images are resized into 224x224

pixels to ensure uniformity across the dataset.

-Color Conversion: Depending on the require-

ments of the pre-trained models, images may be

converted to a speciﬁc color space (e.g., RGB,

grayscale). This ensures compatibility with the

input speciﬁcations of the chosen models.

• Data Augmentation: Data augmentation expand

the dataset and improve the model’s robustness.

This phase includes:

-Rotations: Randomly rotating images helps the

model learn invariant features and makes it less

sensitive to the orientation of the fruit.

-Flips: Horizontal and vertical ﬂipping can en-

hance the diversity of the dataset, allowing the

model to generalize better.

-Zooms: Randomly zooming in or out on im-

ages can help the model learn features at different

scales, improving its ability to recognize diseases

regardless of the fruit’s size in the image.

These techniques effectively counteract overﬁt-

ting by introducing variability into the training set,

ensuring the model learns to identify diseases un-

der different conditions.

• Train-Test Split: The dataset then undergoes

some pre-processing and augmentation, before

splitting into training and testing sets; this is often

done in an 80-20 split. This way, the model can

learn from a large quantity of data while reserving

portions to test its performance objectively.

• Transfer Learning and Lightweight Models:

In this phase, lightweight models such as Mo-

bileNetV2, InceptionV3 and VGG16 are em-

ployed using TL techniques. This involves:

-Utilizing Pre-trained Weights: Models that have

been pre-trained on large datasets (e.g., Ima-

geNet) are ﬁne-tuned on the fruit disease dataset.

This allows the models to leverage learned fea-

tures from previous tasks, signiﬁcantly improving

performance while requiring less training data.

-Selective Freezing of Layers: Certain initial lay-

ers of the pre-trained model are frozen, while the

ﬁnal layers are retrained on the new dataset. This

helps retain critical features while adapting the

model to the speciﬁc task of fruit disease classi-

ﬁcation.

• Performance Evaluation: Finally, the perfor-

mance of the models is evaluated using several

metrics, including:

-Accuracy and Loss Curves: Visualizations of

the model’s accuracy and loss over epochs dur-

ing training help assess the learning process and

detect potential overﬁtting or underﬁtting issues.

This comprehensive methodology ensures ro-

bust and efﬁcient classiﬁcation of fruit diseases,

paving the way for practical applications in

precision agriculture, particularly in resource-

constrained settings.

Fruit Disease Detection Using Lightweight Transfer Learning Techniques

501

4 ALGORITHMS

4.1 MobileNet V2

MobileNetV2 is a lightweight CNN suitable for mo-

bile and embedded vision applications. It is an im-

proved version of its original architecture, which

Google introduced in 2018. Efﬁciency: With respect

to efﬁciency, MobileNetV2 has been designed to be

highly efﬁcient for both computation and low mem-

ory while maintaining high accuracy.

• Architecture: MobileNetV2 employs an inverted

residual structure that enhances efﬁciency. This

structure includes two main components:

-Depthwise Separable Convolutions: Mo-

bileNetV2 replaces standard convolutions with

depthwise separable convolutions, dividing the

convolution process into a depthwise convolution

and pointwise convolution.

-Inverted Residual Blocks: These blocks consist

of a lightweight depthwise separable convolution

followed by linear bottlenecks. This structure al-

lows for more efﬁcient computation and helps pre-

serve spatial information.

• Activation Function: Normally, MobileNetV2

used the ReLU6 activation function, which is

some sort of modiﬁcation from the ReLU func-

tion where its maximum output is limited to 6.

The latter was supposed to weaken the outlier’s

effect and helpful for quantization.

• Pre-processing: The input images for Mo-

bileNetV2 should be resized to 224×224 pixels,

while the pixel values are often normalized.

4.2 VGG16

VGG16 is a CNN architecture. It is known for sim-

plicity in addition to effectiveness for the Image Clas-

siﬁcation tasks.

• Architecture: VGG16 consists of 16 weight lay-

ers, which include:

- 13 convolutional layers

- 5 max-pooling layers

- 3 fully connected layers (also referred to as

dense layers) at the end.

Max-pooling layers are employed after convolu-

tional layers to reduce the spatial dimensions of

the feature maps and to downsample the input,

allowing for a reduction in computational load

while retaining important information.

• Activation Function: ReLU stands as an acti-

vation function after each convolutional layer in

VGG16. It helps the model to learn complex pat-

terns through this non-linearity.

• Depth and Complexity: Considering VGG16, a

depth of 16 layers lets it learn a rich hierarchy

from low-level edges and textures to high-level

concepts such as shape and object.

• Fully Connected Layers: The feature maps after

convolution and pooling are ﬂattened and fed to

the three fully connected layers.

• Pre-processing: The input images to VGG16 are

resized to 224×224 pixels and normalized-mean

subtraction is performed.

4.3 Inception V3

GoogLeNet, also referred to as the Inception Net-

work, is a CNN computational architecture presented

by Google in 2014. The Inception architecture lever-

ages multiscale features together with effectively us-

ing computing resources.

• Architecture: The core innovation of the Incep-

tion architecture is the Inception module, which

allows the model to learn features at various

scales. Each module contains multiple parallel

convolutional ﬁlters of different sizes (1x1, 3x3,

5x5) and a pooling operation (typically max pool-

ing).

• 1x1 Convolutions: The use of 1x1 convolutions

serves several purposes:

– Dimensionality reduction: They reduce the

number of input channels before applying

larger convolutions (3x3, 5x5), which helps de-

crease the computational burden.

– Feature extraction: They allow the model to

learn complex features without increasing the

number of parameters signiﬁcantly.

• Auxiliary Classiﬁers: To combat the vanish-

ing gradient problem and improve gradient ﬂow,

the original Inception model introduced auxiliary

classiﬁers. These are additional branches in the

architecture that act as regularizers, providing ad-

ditional gradients during training.

• Global Average Pooling: Inception uses global

average pooling, which reduces the feature map to

a single value. This approach decreases the num-

ber of parameters and helps mitigate overﬁtting.

INCOFT 2025 - International Conference on Futuristic Technology

502

5 RESULTS ANALYSIS

5.1 Performance Parameters

Several performance metrics are used to assess the ef-

fectiveness of fruit disease classiﬁcation algorithms.

Below are key performance parameters along with

their formulas:

Table 1: PERFORMANCE PARAMETERS

Parameter Formula

Accuracy

T P+T N

T P+T N+FP+FN

Precision

T P

T P+FP

Recall

T P

T P+FN

F1-Score

2×Precision×Recall

Precision+Recall

Where;

TP = True Positives (correctly predicted positive

cases)

TN = True Negatives (correctly predicted negative

cases)

FP = False Positives (incorrectly predicted positive

cases)

FN= False Negatives (incorrectly predicted negative

cases)

5.2 Results

Results depict a signiﬁcant improvement in met-

rics performance for the lightweight MobileNetV2

model, showing its efﬁciency in this application. Mo-

bileNetV2 achieved the highest among others in terms

of performance and efﬁciency as 95% accuracy. This

remarkable accuracy indicates that MobileNetV2 is

successful in identifying the most relevant features

within the dataset, which it does with a small com-

putational cost, making it a good candidate to be put

into practice in resource-constrained environments.

Overall, results conﬁrm the advantages of applying

lightweight transfer learning models, particularly Mo-

bileNetV2, for high classiﬁcation accuracy and en-

suring operational efﬁciency. This progress brings

very nice insights into the implementation of preci-

sion agriculture technologies, proving that effective

fruit disease detection might not demand computa-

tionally expensive resources.

Figure 3, Figure 4 and Figure 5 shows the

accuracy and loss curve of Lightweight Mo-

bileNet Model, Lightweight Inception Model and

Lightweight VGG16 model.

Results are shown in Figure 6, comparing all the

models tested in this work. From these results, it

can be noticed that, among all models, the light ver-

sion of MobileNetV2 reached the highest accuracy

Figure 3: Accuracy and Loss Curve of Lightweight Mo-

bileNet V2 Model

Figure 4: Accuracy and Loss Curve of Lightweight Incep-

tion V3 Model

Figure 5: Accuracy and Loss Curve of Lightweight VGG16

Model

Figure 6: Performance Comparison of Transfer Learning

Models

and F1-score. The MobileNetV2 model reached an

incredible 95% in accuracy, beating even the best clas-

sical transfer learning models. This success is at-

tributed to its neat architecture: it allows for effective

feature extraction while saving computations, hence

making a model feasible in resource-constrained set-

tings. Conclusions: The results conﬁrm capability for

a lightweight transfer learning model, such as Mo-

bileNetV2, in advancing agricultural applications to

provide farmers with timely and accurate fruit disease

detection without relying on high-end computing re-

Fruit Disease Detection Using Lightweight Transfer Learning Techniques

503

sources.

5.3 Computational Beneﬁts

Lightweight models like MobileNetV2, Lightweight

InceptionV3, and Lightweight VGG16 offer signif-

icant computational beneﬁts over regular models.

These models also provide faster inference with lower

latency, enabling real-time processing on resource-

constrained devices. Their lower computational over-

head and efﬁcient use of resources reduce power

consumption. Additionally, lightweight models are

more scalable and easier to optimize for mobile and

embedded systems, supporting hardware accelerators

like DSPs, GPUs, and NPUs. This makes them

well-suited for edge computing and real-time applica-

tions. Below Table shows the parameters comparison

of traditional transfer learning models and proposed

lightweight transfer learning model.

Table 2: MODEL PARAMETERS COMPARISON

Models Traditional

(Params)

Lightweight

(Params)

VGG16 ˜138M ˜16M

MobileNet

˜3.4M ˜2.9M

Inception V3 ˜23.8M ˜6M

6 CONCLUSION

This research addresses the importance of rapid

and precise fruit disease detection, whose essence is

paramount, as it enhances agricultural productivity

by reducing crop loss. By using light-weight trans-

fer learning with pre-trained models, effective clas-

siﬁcation models with a good balance between ac-

curacy and computational efﬁciency were developed

using MobileNetV2 and VGG16. Therefore, subse-

quent to this, our approach in this paper consists of

rigorous steps of preprocessing, effective augmenta-

tion strategies, and judicious model performance eval-

uation performance in terms of various metrics such

as precision, accuracy, recall, and F1-score. Notably,

the lightweight MobileNetV2 model stood out with

the highest accuracy of 95%, thus representing su-

periority against traditional transfer learning meth-

ods. It is crystal clear from the comparative analy-

sis that lightweight models achieved higher accuracy

and F1 scores with MobileNets, hence, can make a

robust solution for deployable real-time applications

in resource-constrained agricultural settings. These

results add valuably to the insights on implement-

ing technologies in precision agriculture that ﬁnally

help farmers in effective disease management and im-

provement of overall fruit health.

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