Robust Optimization of MobileNet for Plant Disease Classification with Fine Tuned Parameters

Younis, Haseeb; Khan, Muhammad Zeeshan; Khan, Muhammad Usman Ghani; Mukhtar, Hamza

doi:10.1109/icai52203.2021.9445261

Cited by 6 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning's progress has turned computer vision into an agricultural vision for target detection and image semantic segmentation, giving the best results [19]. Because of its ability to handle large amounts of data, deep learning has proven to be a very effective tool [20]. The interest in using hidden layers has exceeded traditional techniques in terms of popularity, particularly in object recognition, classification, and detection [21].…”

Section: Introductionmentioning

confidence: 99%

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

et al. 2023

View full text Add to dashboard Cite

Deep learning has shown much promise in target identification in recent years, and it's becoming more popular in agriculture, where fig fruit detection and counting have become important. In this study, a systematic literature review (SLR) is utilised to evaluate a deep learning algorithm for detecting and counting fig fruits. The SLR is based on the widely used 'Reporting Standards for Systematic Evidence Synthetics' (ROSES) review process. The study starts by formulating the research questions, and the proposed SLR approach is critically discussed until the data abstraction and analysis process is completed. Following that, 33 relevant research involving the agriculture sector, fruit, were selected from many studies. IEEE, Scopus, and Web of Sciences are three databases to investigate. Due to the lack of fig fruit research, fruit and vegetable studies have been included because they use similar methods and processes. The SLR found that various deep learning algorithms can count fig fruit in the field. Furthermore, as most approaches obtained acceptable results, deep learning's performance is acceptable in F1-score and average precision (AP), higher than 80%. Moreover, improvements can be produced by enhancing the existing deep learning model with the personal dataset.

show abstract

Section: Introductionmentioning

confidence: 99%