Classification of grapevine leaves images using VGG-16 and VGG-19 deep learning nets

Abstract

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classification by adapting VGG-16 net and VGG-19 net models and subsequently identifying the optimal performer between the two nets during the classification process. A publicly available dataset comprising 500 images categorized into 5 distinct classes (100 images per class), was utilized in this work. The obtained empirical outcomes demonstrate a remarkable accuracy rate of 99.6% for the VGG-16 net model, while VGG-19 net achieves a 100% accuracy rate. Based on these findings, it can be inferred that VGG-19 net exhibits superior performance in classifying images of grapevine leaves compared to the VGG-16 net.

Deep Learning and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review

“…Abassi and Jalal [30], the only study published using an ML-based approach after 2023, achieved an accuracy of 83. 20%, although this result was lower than all the other studies that proposed DL-based approaches to classify the same dataset [7,[38][39][40][41][42]46]. In fact, the ability of DL-based approaches to automatically learn to extract useful features has been making exponential advances in computer vision since 2012, with the publication of Krizhevsky et al [23].…”

“…Carneiro et al [49] stated that the use of ViTs outperformed previous results, however, at the cost of increased computational needs for training and inference. On the other hand, Kunduracioglu and Pacal [38] achieved 100% accuracy for both CNN (Inception V4) and transformers (Swin Transformers), but Rajab et al [39] obtained the same result on the same dataset using an older model (VGG-19). Thus, there is still space to explore the impact of using transformers on grape variety identification.…”

“…This dataset consists of 300 images belonging to 6 grape varieties. Koklu et al [7] provided the dataset used and, more recently, studies [30,[39][40][41][42]46] have followed and explored the same dataset. This dataset consists of 5 classes and was acquired in a controlled environment, resulting in 500 images.…”

“…As in other fields of research, in grape variety identification, DL-based approaches outperformed ML-based approaches in both the number of publications and performance (see Figure 4). Among the included studies that used DL and images, the worst performance was achieved by Pereira et al [58] with an accuracy of 77.30%, while other studies achieved 100% [37][38][39][40]57]. In this respect, the architecture used by Pereira et al [58] was AlexNet, the first deep CNN proposed in the literature in 2012 [23].…”