An efficient deep learning model for cultivar identification of a pistachio tree

Abstract

PurposeThis paper proposes a novel deep learning based method towards the identification of a pistachio tree cultivar from its image.Design/methodology/approachThe investigated scope of this study includes Iranian commercial pistachios (Jumbo, Long, Round and Super long) trees. Effective use of high-resolution images with standard deep models is addressed in this study. A novel image patches extraction method is also used to boost the number of samples and dataset augmentation. In the proposed method, handcrafted ORB features are used to detect and extract patches which may contain identifiable information. An innovative algorithm is proposed for searching and extracting these patches. After extracting patches from initial images, a Convolutional Neural Network, named EfficientNet-B1, was fine-tuned on it. In the testing phase, several patches were extracted from the prompted image using the ORB-based method, and the results of their prediction were consolidated. In this method, patch prediction scores were in descending order, sorted by the highest score in a list, and finally, the average of a few list tops was calculated and the final decision was made.FindingsExamining the proposed method on the test images led to an achievement of a recognition rate of 97.2% accuracy. Investigation of decision-making in the test dataset could reveal that this method outperformed human experts.Originality/valueCultivar identification using deep learning methods, due to their high recognition speed, lack of specialist requirement, and independence from human decision-making error is considered as a breakthrough in horticultural science. Variety cultivars of pistachio trees possess variant characteristics or traits, accordingly recognising cultivars is crucial to reduce the costs, prevent damages and harvest the optimal yields.

ICPTC: Iranian commercial pistachio tree cultivars standard dataset

“…To enhance prediction accuracy grid-based patches are also extracted and used. To see more details on the proposed model please refer to the related research paper [1] .…”

“…A part of the chilling requirement can be compensated by applying some chemical substances. The amount and time of use of these substances are different for different cultivars of pistachios, so the correct identification of the tree cultivar is very important to prevent possible damage [1] .…”

“…Not knowing the pistachio cultivar, can cause irreparable damage. So, the pistachio trees cultivar recognition is crucial in the process of harvesting optimal yields as well as cost savings [1] .

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“… This paper contains datasets related to the “An efficient deep learning model for cultivar identification of a pistachio tree” [1] . There are about 11 species of pistachio that often have a high commercial and economic value in Iran and United States.…”

ACHENY: A standard Chenopodiaceae image dataset for deep learning models

“…Second, a model is obtained from EfficientNet-B1 [4] by fine-tuning, which is previously trained on ImageNet [7] , and is named Efficient-ACHENY. Google's EfficientNet obtain a model by compound scaling up a baseline model [8] . In Efficient-ACHENY the model's width and depth are scaled up according to the associated input size (224 × 224 × 3) which leads to a high-performing model but it increases computational complexity.…”