Plant Disease Detection Using Deep Learning

Published Online on December 8, 2023 by MECS Press

Agriculture is the main source of prosperity for most of the countries and their economic growth. Therefore, plant diseases and infections spread in the plant affect the quantity and quality of the plant, which makes it a threat to food security [1]. These threats have increased more than ever before due to climate changes and global trade. And that preventive treatment will not be effective in some cases of diseases. Therefore, it is necessary to pay attention to protecting plants sustain food security. Therefore, the solution is the early follow-up and correct diagnosis of the diseased plant by using advanced and automatic plant follow-up systems to reduce the percentage of crop losses and increase the amount of production [2].

The paramount research objectives center on the development of advanced and precise AI-based solutions for the early detection and management of plant diseases, an endeavor with far-reaching implications for fostering agricultural development and upholding global food security. These objectives come to the forefront amidst the burgeoning advancements in computer science and computer vision, which have unveiled novel methods for diagnosing infected plants, simplifying disease classification, and refining treatment strategies. This concerted research effort is poised to harness the synergy between technological innovation and agricultural needs, ultimately paving the way for transformative solutions that have the potential to revolutionize the safeguarding of crops on a global scale.

Existing solutions in the field of plant disease detection encompass a multifaceted approach. They begin by introducing emerging solutions such as early follow-up and precise diagnosis of diseased plants through cutting-edge automatic plant monitoring systems. Concurrently, these solutions delve into the potential of fine-tuning pre-trained models, specifically leveraging the power of pre-trained models, to enhance the accuracy of convolutional neural network (CNN)-based models in detecting plant diseases across diverse and challenging environments [3]. The utilization of general datasets during training. This pursuit of advanced and precise AI-driven solutions holds pivotal significance in propelling agricultural progress and ensuring global food security. Further contextualizing these advancements, the paragraph references the historical application of deep learning models, particularly CNNs, in plant disease detection, with a nod to prior research employing pre-trained CNNs. Challenges, such as dataset limitations, are acknowledged, and various studies are cited, encompassing transfer learning, generative networks, and data augmentation strategies to enhance disease detection accuracy [4]. Additionally, a noteworthy research endeavor examining families of detectors is highlighted, collectively offering a comprehensive overview of the existing landscape in the realm of plant disease detection.

The identification of the best solution in this study centers on the utilization of EfficientNetV2S, a pre-trained model, which emerges as a promising candidate to address the limitations encountered by previous models and attain heightened accuracy in the detection of plant diseases. Within this investigation, a suite of models was deployed for comparative analysis of classification accuracy while striving for optimal time complexity. These models were trained with a strategy involving layer freezing, thereby preserving the model's original parameters while fine-tuning them to extract essential features and allocate them to their respective categories [5]. Implemented on the Plant Disease Detection Dataset, low-resolution images were employed to bolster the model's resilience against natural environmental conditions. Remarkably, the study achieved exceptional accuracy and reduced loss in simulated real-world conditions through hyperparameter tuning of a pre-trained model, such as EfficientNet, renowned for its use of Depthwise Separable Convolution, rendering it suitable for embedded projects requiring a lightweight model. Focused on image classification tasks necessitating dimension scaling, the model underwent training and testing on an RGB dataset comprising healthy and unhealthy plants, encompassing 38 disease categories across 14 distinct plant species, with each class consistently featuring both healthy and infected specimens [6]. Notably, the model's initial pre-training on the diverse ImageNet dataset expedited the acquisition of common features, such as edges and lines, facilitating efficient learning across datasets, and was complemented by a classification head trained specifically on the plant disease dataset, thereby enabling accurate disease classification.

The main limitation encountered in this context is the inherent challenge of effectively classifying diverse plant diseases, primarily stemming from constraints imposed by limited dataset resources, thus accentuating the imperative for innovative methodologies to transform these datasets into realistic simulations of natural environmental conditions [7]. The pursuit of an accurate model capable of classifying multiple plant diseases within a unified network framework constitutes a pivotal stride towards enhancing food production quality and mitigating economic losses. Paramount to this endeavor is the model's ability to achieve exceptional accuracy under simulated natural environmental conditions while minimizing losses when processing inherently noisy images. Moreover, the imperative lies in reducing computational training time and addressing the issue of overfitting to render the model practically deployable. To attain these objectives, the utilization of pre-trained models grounded in convolutional neural networks (CNNs), coupled with meticulous hyperparameter fine-tuning, becomes essential to elevate disease detection accuracy amidst lower-resolution images, complex backgrounds, shadow-veiled leaves, varied textures, and dynamic brightness fluctuations. Ultimately, the development of such a model assumes a pivotal role in early disease detection and management, thereby making significant strides towards advancing agricultural development and addressing the pressing challenge of sustaining the burgeoning global population.

this study seeks to create a model that not only achieves high accuracy in a simulated natural environment but also addresses lower resolution images, crowded backgrounds, shadows on leaves, diverse textures, and variations in brightness. Additionally, the aim is to reduce computation time for training and overcome the problem of overfitting.

The results of an experiment evaluating the performance of the EfficientnetV2S model in handling the limitations of noisy datasets for image-based plant disease identification. The EfficientnetV2S model achieved a validation accuracy of 95.01% on the noisy dataset. The result demonstrates the effectiveness of the model in dealing with imagebased plant disease identification tasks that involve noisy images, highlighting their robustness and potential for real-world applications.

The following is a summary of this paper's significant contributions:

Providing an accurate model for classifying multiple plant disease through the same network.

Getting a well-trained model that achieves a high accuracy on a simulated natural environment condition, and lower loss on noisy images.

Reduce the computation time for the training process and solving the problem of overfitting.

The rest of the paper is arranged as follows. Section 2 illustrates the related work. Presented model is applied in Section 3. Experimental in Section 4. The results and discussion are discussed in Section 5. Section 6 presents the main conclusions of this study, and the future work.

2.    Related Works

Ignoring early indicators of plant diseases in the agricultural field may lead to losses in food crops and eventually lead to the collapse of the global economy [8]. This section provides a previous work in the field of plant disease detection.

Jadhav et al. proposed a model for a plant disease using CNNs [9]. In this paper, AlexNet and GoogleNet pretrained convolutional neural networks were used to present an effective soybean disease identification technique based on transfer learning strategies. But the model slipped in Classification diversity. Many current models focus on defining a single class of plants disease instead of building a model to classify different plant diseases. This is basically due to reduction of dataset resources to train deep learning (DL) models with diverse plant species.

Table 1. Summary of CNN models that detect the plant disease