Digital evaluation of leaf area of an individual tree canopy in the apple orchard using the LIDAR measurement system

Berk, P.; Stajnko, D.; Belšak, Aleš; Hočevar, Marko

doi:10.1016/j.compag.2019.105158

Cited by 33 publications

(23 citation statements)

References 26 publications

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“…Cultivation of vegetable crops inside a controlled environment chamber and extracting leaf canopy signatures that holds essential information for crop phenotyping has captivated agriculturist and scientist for quite a few generations (Burgos-Artizzu, Ribeiro, Guijarro, & Pajares, 2011;Calangian et al, 2018;de Luna, Dadios, Bandala, & Vicerra, 2019;Hang, Lu, Takagaki, & Mao, 2019;Loresco, Valenzuela, Culaba, & Dadios, 2019;Zou et al, 2019). In this study, it is shown that indoor hydroponic lettuce canopy area can be measured based on numerical image textural feature analysis of Haralick and gray level co-occurrence matrix (Table 1) as compared with morphological pixel feature (Calangian et al, 2018), leaf shape (Saleem, Akhtar, Ahmed, & Qureshi, 2019) and point cloud analysis (Berk, Stajnko, Belsak, & Hocevar, 2020). Instead of using LIDAR technology (Berk, Stajnko, Belsak, & Hocevar, 2020) and a multispectral camera (Fan et al, 2018), a consumer-grade digital camera was used in an image capturing in order to be more available for the general public.…”

Section: Results Of Thresholding For Image Segmentation On Lettucementioning

confidence: 96%

“…In this study, it is shown that indoor hydroponic lettuce canopy area can be measured based on numerical image textural feature analysis of Haralick and gray level co-occurrence matrix (Table 1) as compared with morphological pixel feature (Calangian et al, 2018), leaf shape (Saleem, Akhtar, Ahmed, & Qureshi, 2019) and point cloud analysis (Berk, Stajnko, Belsak, & Hocevar, 2020). Instead of using LIDAR technology (Berk, Stajnko, Belsak, & Hocevar, 2020) and a multispectral camera (Fan et al, 2018), a consumer-grade digital camera was used in an image capturing in order to be more available for the general public.…”

Section: Results Of Thresholding For Image Segmentation On Lettucementioning

confidence: 99%

“…For nonvision systems, chlorophyll pigments of lettuce leaf were correlated with the surface area using plasma technologies (Stoleru et al, 2018). The light detection and ranging (LIDAR) data points of plant volume were utilized for the development of a regression model correlating it to leaf area with a maximum correlation coefficient of 0.87 (Berk, Stajnko, Belsak, & Hocevar, 2020;Sinha, Padalia, Dasgupta, Verrelst, & Rivera, 2020). Leaf area and its consumption of carbon dioxide (CO 2 ) were modeled to estimate the demanding vegetation area (Jin et al, 2013;Rahman, Duursma, Muktadir, Roberts, & Atwell, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Lettuce Canopy Area Measurement Using Static Supervised Neural Networks Based on Numerical Image Textural Feature Analysis of Haralick and Gray Level Co-Occurrence Matrixs

et al. 2020

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Journal of Agricultural Science www.agrivita.ub.ac.id based on the Bureau of Plant Industry-National Crop Research and Development Center (BPI-NCRDC) of the Department of Agriculture (DOA) due to its significant impact on medicinal, culinary, and health perspectives. Loose-leaf lettuce makes up its harvest growth stage after 45 to 90 days after transplanting that normally takes 12 to 14 days from germination. The sensitive way of growing lettuce is inclusive to the beneficial application of smart farming (Ge, Zhou, Kellomäki, Peltola, & Wang, 2011; Jindarat & Wuttidittachotti, 2015). It also demands a heuristic approach of monitoring to enhance its agricultural process. The lettuce growth, similar to the other higher value leafy crops, is primarily affected by light-dependent and gas-dependent reactions. The growth and development changes are verified to the changes occurred on its fresh weight, dry weight, volume, height, surface area, root zone depth ARTICLE INFO

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Section: Results Of Thresholding For Image Segmentation On Lettucementioning

confidence: 96%

Section: Results Of Thresholding For Image Segmentation On Lettucementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lettuce Canopy Area Measurement Using Static Supervised Neural Networks Based on Numerical Image Textural Feature Analysis of Haralick and Gray Level Co-Occurrence Matrixs

et al. 2020

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“…This study’s findings established initial guidelines for mapping leaf area density in dense tree areas. Berk et al [ 55 ] reconstructed the tree canopy for calculating LAD through LiDAR sensor and the volume element method. The trapezoidal method was used to calculate individual volumes of tree canopy volume elements.…”

Section: Core Components and Technologies For Precision Sprayingmentioning

confidence: 99%

Opportunities and Possibilities of Developing an Advanced Precision Spraying System for Tree Fruits

Mahmud

Zahid

et al. 2021

Sensors

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Reducing risk from pesticide applications has been gaining serious attention in the last few decades due to the significant damage to human health, environment, and ecosystems. Pesticide applications are an essential part of current agriculture, enhancing cultivated crop productivity and quality and preventing losses of up to 45% of the world food supply. However, inappropriate and excessive use of pesticides is a major rising concern. Precision spraying addresses these concerns by precisely and efficiently applying pesticides to the target area and substantially reducing pesticide usage while maintaining efficacy at preventing crop losses. This review provides a systematic summary of current technologies used for precision spraying in tree fruits and highlights their potential, briefly discusses factors affecting spraying parameters, and concludes with possible solutions to reduce excessive agrochemical uses. We conclude there is a critical need for appropriate sensing techniques that can accurately detect the target. In addition, air jet velocity, travel speed, wind speed and direction, droplet size, and canopy characteristics need to be considered for successful droplet deposition by the spraying system. Assessment of terrain is important when field elevation has significant variability. Control of airflow during spraying is another important parameter that needs to be considered. Incorporation of these variables in precision spraying systems will optimize spray decisions and help reduce excessive agrochemical applications.

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“…As the pillar of wine industry, grape is receiving increasingly interest and now has genome sequence from thousands of germplasm [2][3][4]. Conventionally, different germplasm (genotypes) were classified according to their working phenotypes by designated biologists, where "manual" images were used in terms of canopy architecture, leaf area, and other functions [5][6][7]. These functions can be calculated manually or through a customized image processing algorithm.…”

Section: Introductionmentioning

confidence: 99%

Identification of grapevine(Vitis vinifera L.)cultivars by vine leaf image via deep learning and mobile devices

Liu

Guo-qian

et al. 2020

Preprint

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Background Traditional vine variety identification methods usually rely on destructive sampling of vine leaves followed by physical, physiological, biochemical and molecular measurement, which is destructive, time-consuming, labor-intensive and requires experienced grape phenotype analysts. To mitigate these problems, this study aims to develop an application (App) running on Android client to identify wine grape automatically and in real time, which can help the growers to quickly obtain the variety information. Results Experimental results show that all Convolutional Neural Network (CNN) classification algorithms can achieve an accuracy of over 94% for 21 categories on validation data, which proves the feasibility of using transfer learning to identify grape species in field environments. In particular, the classification model with the highest average accuracy is Googlenet (97.4%) with learning rate (0.001), mini-batch size (32) and maximum number of epochs (80). Testing results of the App on Android device also confirms these results. Conclusions An App running on Android client is developed to identify the wine grape in real time and field condition, which can help the growers to quickly obtain the variety information of wine grape. A total of 4200 leaf images were first collected in the field environment, which contain 21 types of typical grapes. Then both image complement preprocessing and data augmentation are adopted to enhance image features and augment the training dataset. On this basis, a number of typical CNN models (including VGG-16, DenseNet, ResNet101, ResNet18, and GoogLeNet) are compared in transfer learning training to identify the suitable one while with model parameter tuning. It is shown that GoogLeNet model outperformed other models in terms of accuracy, model complexity, and robustness with a fine-tuned accuracy of 99.91%. The effect of image complement preprocessing is also assessed by using Grad-CAM algorithm. The developed App is also shown to be feasible for real-life applications with a processing time of less than 1 second.

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Digital evaluation of leaf area of an individual tree canopy in the apple orchard using the LIDAR measurement system

Cited by 33 publications

References 26 publications

Lettuce Canopy Area Measurement Using Static Supervised Neural Networks Based on Numerical Image Textural Feature Analysis of Haralick and Gray Level Co-Occurrence Matrixs

Lettuce Canopy Area Measurement Using Static Supervised Neural Networks Based on Numerical Image Textural Feature Analysis of Haralick and Gray Level Co-Occurrence Matrixs

Opportunities and Possibilities of Developing an Advanced Precision Spraying System for Tree Fruits

Identification of grapevine(Vitis vinifera L.)cultivars by vine leaf image via deep learning and mobile devices

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