Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture Applications

Abstract

The crop water stress index (CWSI) is one of the parameters measured in deficit irrigation and it is obtained from crop canopy temperature. However, image segmentation is required for non-leaf region exclusion in temperature measurement, as it is critical to obtain the temperature values for the calculation of the CWSI. To this end, two image-segmentation models based on support vector machine (SVM) and deep learning have been studied in this article. The models have been trained with different parameters (encoder depth, optimizer, learning rate, weight decay, validation frequency and validation patience), and several indicators (accuracy, precision, recall and F1 score/dice coefficient), as well as prediction, training and data preparation times are discussed. The results of the F1 score indicator are 83.11% for SVM and 86.27% for deep-learning models. More accurate results are expected for the deep-learning model by increasing the dataset, whereas the SVM model is worthwhile in terms of reduced data preparation times.

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

“…It is preferable due to the low-cost and easy to use sensor [120]. DL has been successfully used for segmenting plant and background in RGB images for plant water stress detection with good results [121]. However, lack of spectral bands, with only three basic colours in the image, limits detailed information about the status of the plant [122].…”

Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

“…Leaf segmentation with a complex background was also a challenging and complicated task. Few researchers worked on different plant leaf segmentation, such as [28,29]. Gimenez-Gallego et al [28] segment the multiple tree's leaves like lemon, orange, almond, olive, loquat, fig, cherry and walnut trees from the natural background using the SVM and SegNet deep learning model.…”

Peer Review #1 of "CapPlant: a capsule network based framework for plant disease classification (v0.1)"

“…Deep Leaning (DL) can be utilized as it constitutes a modern and state-of-the-art technique for image processing and data analysis with great potential and promising results (Kamilaris and Prenafeta-Boldú, 2018). DL has been successfully applied in various domains like healthcare (Miotto et al, 2018), automatic machine translation (Singh et al, 2017), automatic text generation (Pawade et al, 2018), image recognition (Satapathy et al, 2019) and agriculture (Giménez-Gallego et al, 2020;Zheng et al, 2019b) etc.…”