An Efficient Deep Learning Mechanism for the Recognition of Olive Trees in Jouf Region

Abstract

Olive trees grow all over the world in reasonably moderate and dry climates, making them fortunate and medicinal. Pesticides are required to improve crop quality and productivity. Olive trees have had important cultural and economic significance since the early pre-Roman era. In 2019, Al-Jouf region in a Kingdom of Saudi Arabia’s north achieved global prominence by breaking a Guinness World Record for having more number of olive trees in a world. Unmanned aerial systems (UAS) were increasingly being used in aerial sensing activities. However, sensing data must be processed further before it can be used. This processing necessitates a huge amount of computational power as well as the time until transmission. Accurately measuring the biovolume of trees is an initial step in monitoring their effectiveness in olive output and health. To overcome these issues, we initially formed a large scale of olive database for deep learning technology and applications. The collection comprises 250 RGB photos captured throughout Al-Jouf, KSA. This paper employs among the greatest efficient deep learning occurrence segmentation techniques (Mask Regional-CNN) with photos from unmanned aerial vehicles (UAVs) to calculate the biovolume of single olive trees. Then, using satellite imagery, we present an actual deep learning method (SwinTU-net) for identifying and counting of olive trees. SwinTU-net is a U-net-like network that includes encoding, decoding, and skipping links. SwinTU-net’s essential unit for learning locally and globally semantic features is the Swin Transformer blocks. Then, we tested the method on photos with several wavelength channels (red, greenish, blues, and infrared region) and vegetation indexes (NDVI and GNDVI). The effectiveness of RGB images is evaluated at the two spatial rulings: 3 cm /pixel and 13 cm /pixel, whereas NDVI and GNDV images have only been evaluated at 13 cm/pixel. As a result of integrating all datasets of GNDVI and NDVI, all generated mask regional-CNN-based systems performed well in segmenting tree crowns (F1-measure from 95.0 to 98.0 percent). Based on ground truth readings in a group of trees, a calculated biovolume was 82 percent accurate. These findings support all usage of NDVI and GNDVI spectrum indices in UAV pictures to accurately estimate the biovolume of distributed trees including olive trees.

Advancements in Remote Sensing Imagery Applications for Precision Management in Olive Growing: A Systematic Review

“…Ye et al [157] applied a DL approach (UAV-U2-Net) obtaining high precision (97.69% precision, 98.55% recall). Alshammari and Shahin [154] introduced Swin-TU-net, achieving 98.4% accuracy with low-cost sensors. Martínez-Ruedas et al [222] classified orchard management systems with DL, presenting overall accuracies exceeding 0.8.…”

Evaluation of Land Suitability for Olive (Olea europaea L.) Cultivation Using the Random Forest Algorithm

“…In that study, in which many ML models were compared, suitable areas for tea cultivation were determined by using 12 parameters including climate, soil, land, and economy factors and the RF algorithm. In the literature, there are studies carried out to determine the distribution of olive trees in a particular region or to determine olive tree varieties using ML or DL algorithms [37][38][39][40]. However, to the best of our knowledge, no study has been performed to determine suitable areas for olive cultivation using ML algorithms.…”