Abstract
The olive fruit fly can damage up to 100% of the harvested fruit and can cause up to 80% reduction of the value of the resulting olive oil. Therefore, it is important to early detect its presence in the olive orchard to take the appropriate chemical or biological countermeasures as early as possible. Traps filled with attractant pheromones are typically deployed across the orchard to attract and capture the flies. Traditionally, the captured flies were manually counted which is error prone. Recently, the traps are employed with cameras and communication devices to send pictures of the captured flies to experts for analysis which is also error prone and inefficient. Consequently, machine and deep learning have been exploited to develop fully automated and accurate detection that does not include human in the loop. Such a learning problem is challenging due to the small size of the detected object, the differences in the light conditions at which pictures were taken, and the lack of enough data to train the learning model. In this paper, we present a deep learning framework for detecting and counting the number of olive fruit flies that exploits data augmentation to increase the dataset size, includes negative samples in the training to improve the detection accuracy, and normalizes the images to the color of the trap background, i.e., yellow, to unify the illumination conditions. The results of the proposed framework show a precision of 0.84, a recall of 0.97, an F1-score of 0.9 and mean Average Precision (mAP) of 96.68% which significantly outperforms existing pest detection systems.
Highlights
Olive is a fruit that has a great economic and health benefits
OLIVE FRUIT FLY DETECTION FRAMEWORK In this paper, we present a framework to detect and count the olive fruit flies in the images captured by the smart pheromone traps deployed across the olive groves
2) THE MODIFIED You Only Look Once (YOLO) we evaluate the impact of modifying the baseline YOLO architecture for detecting small objects to fit our application
Summary
Olive is a fruit that has a great economic and health benefits. It has been grown in the Mediterranean region for 7000 years with Spain being the largest olive producer worldwide [1], and Egypt being ranked first in the world production of table olives in 2020 [2]. The existing automatic pest detection techniques are based on either deep learning [4]-[7], machine learning [8], image processing [9]-[12], spectroscopy [13]-[15] or optoacoustic [16]. Khattab: YOLO-Based Deep Learning Framework for Olive Fruit Fly Detection and Counting classification algorithm [8]. Deep learning techniques developed for other precision agriculture problems such plant disease detection [17]-[19] and fruit picking using binocular vision detection [20] and three-dimensional reconstruction [21][22], or even for the detection of other objects such as faces [23] are beyond the paper scope This is mainly because of the significant difference in the size and features of the detected objects. We propose a highly accurate framework for olive fruit fly detection.
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