A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review

Abstract

Crops’ production and quality of yields are heavily affected by crop diseases which cause adverse impacts on food security as well as economic losses. In India, agriculture is a prime source of income in most rural areas. Hence, there is an intense need to employ novel and accurate computer vision-based techniques for automatic crop disease detection and their classification so that prophylactic actions can be recommended in a timely manner. In literature, numerous computer vision-based techniques by utilizing divergent combinations of machine learning, deep learning, CNN, and various image-processing techniques along with their associated merits and demerits have already been discussed. In this study, we systematically reviewed recent research studies undertaken by a variety of scholars and researchers of fungal and bacterial plant disease detection and classification and summarized them based on vital parameters like type of crop utilized, deep learning/machine learning architecture used, dataset utilized for experiments, performance matrices, types of disease detected and classified, and highest accuracy achieved by the model. As per the analysis carried out, in the category of machine learning-based approaches, 70% of studies utilized real-field plant leaf images and 30% utilized laboratory condition plant leaf images for disease classification while in the case of deep learning-based approaches, 55% studied employed laboratory-conditioned images from the PlantVillage dataset, 25% utilized real-field images, and 20% utilized open image datasets. The average accuracy attained with deep learning-based approaches is quite higher at 98.8% as compared to machine learning-based approaches at 92.2%. In the case of deep learning-based methods, we also analyzed the performances of pretrained and training from scratch models that have been utilized in various studies for plant leaf disease classification. Pretrained models perform better with 99.64% classification accuracy compared to training from scratch models which achieved 98.64% average accuracy. We also highlighted some major issues encountered in the computer vision-based disease detection and classification approach used in literature and provided recommendations that will help and guide researchers to explore new dimensions in crop disease recognition.

Advanced deep learning techniques for early disease prediction in cauliflower plants

“…Deep learning models learn complex patterns and features by being trained on large sets of labelled pictures of healthy and unhealthy plants. This lets those spot even minor signs of disease 9 as most of the time, standard methods of detection can’t get to this level of detail. Additionally, deep learning models can also be tuned and changed to fit different types of crops and diseases.…”

Game Theory-Infused Hybrid CatBoost- Extreme Learning Machine model for Reliable Identification of Rice Leaf Diseases for Advancing Agricultural Surveillance

“…These leaf images are a compilation of three different disease types, including brown spot, bacterial leaf blight and leaf smut Images of affected rice leaf samples that were used in this study. 20% of the images were utilized for testing, and 80% were used for training [22].…”

A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant

“…Despite the availability of plant disease datasets such as the PlantVillage dataset [36], the AgriVision collection [37], and the Plant Disease Identifcation dataset, implementing CNN-based detection algorithms requires large datasets. Past research such as Kumar et al [38], Sladojevic et al [39], and He et al [40] examined the signifcant consequences of crop diseases on food security and economic losses in India's agriculturereliant rural regions. It underscored the requirement for innovative computer vision methods to autonomously identify and categorize these diseases, with studies showing diverse approaches and notable successes, especially in deep learning-based techniques.…”