A Systematic Review of the Current State of Transfer Learning Accelerated CNN-Based Plant Leaf Disease Classification

Crops and their produce are vital to the livelihood of humans everywhere. World food security heavily relies on them, but still, even today, hundreds of millions of people world-wide are suffering from hunger. This is why it is essential to ensure that losses to the agricultural yield are kept at a minimum. Plant diseases, however, cause massive losses to the possible yield every year, rendering large amounts of the planted crops useless. And, if these diseases are not identified early enough, they will further infect more plants and therefore destroy even more yield. This is why plant diseases need to be recognized as fast as possible. Many diseases can be detected via the symptoms present on the plants leaves. As such, traditionally, staff samples and checks plants for their health in field manually. To speed up and increase accuracy, deep learning methods have been proposed to classify plant leaf images by their diseases. To train such models, however, one needs sufficiently large datasets. Such datasets are a gap in the field, with many datasets either being too small, not applicable (wrong plants or diseases), private, or taken under conditions that do not apply to the task at hand. One way to lower the need for more data and to overcome lesser data availability is transfer-learning, which utilizes unrelated rich, big, and available data to train feature extractors, which can then be re-trained to identify plant-leaves, based on the knowledge extracted from the prior task. In this work we will review and analyze a total of 84 convolutional neural network based transfer-learning papers from 2022 to 2025 out of 118 considered using PRISMA and discuss the insights gathered from them. This paper presents statistics on model, dataset, hyperparameter, pre-processing, augmentation, and metrics usage. Also, different methods for improving transfer-learning accelerated convolutional neural network models and training are listed. Additionally, performance comparisons of different prominent models for the field of plant leaf disease classification, as well as performance comparisons of different dataset types will be provided.