Greenhouse horticulture definition

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  • Coldframes and mini-greenhouses
  • Greenhouse Manager
  • Design and Maintenance of Green House 3(2+1)
  • Proposed features/greenhouse horticulture
  • horticulture
  • Greenhouse
  • Display Garden
  • The Meaning of "Horticulture"
  • Local Regulation of Agricultural and Horticultural Operations
  • Growth monitoring of greenhouse lettuce based on a convolutional neural network
WATCH RELATED VIDEO: What is Horticulture Definition of Horticulture Cultivation of crop horticulture

Coldframes and mini-greenhouses

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In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Growth-related traits, such as aboveground biomass and leaf area, are critical indicators to characterize the growth of greenhouse lettuce. Currently, nondestructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features.

In this study, a method for monitoring the growth of greenhouse lettuce was proposed by using digital images and a convolutional neural network CNN. Taking lettuce images as the input, a CNN model was trained to learn the relationship between images and the corresponding growth-related traits, i. To compare the results of the CNN model, widely adopted methods were also used.

The results showed that the values estimated by CNN had good agreement with the actual measurements, with R 2 values of 0. The obtained results showed that the CNN demonstrated superior estimation performance for the flat-type cultivars of Flandria and Tiberius compared with the curled-type cultivar of Locarno. Generalization tests were conducted by using images of Tiberius from another growing season. The results showed that the CNN was still capable of achieving accurate estimation of the growth-related traits, with R 2 values of 0.

The results indicated that a CNN with digital images is a robust tool for the monitoring of the growth of greenhouse lettuce. Growth monitoring is essential for optimizing management and maximizing the production of greenhouse lettuce. Monitoring the growth of greenhouse lettuce by accurately obtaining growth-related traits LFW, LDW, and LA is of great practical significance for improving the yield and quality of lettuce 3.

The traditional methods for measuring growth-related traits, which are relatively straightforward, can achieve relatively accurate results 4. However, the methods require destructive sampling, thus making it time-consuming and laborious 5 , 6 , 7. In recent years, nondestructive monitoring approaches have become a hot research topic.

With the development of computer vision technology, image-based approaches have been widely applied to the nondestructive monitoring of crop growth 6 , 8 , 9 ,Specifically, the image-based approaches extract low-level features from digital images and establish the relationship between the low-level features and manually measured growth-related traits, such as LA, LFW, and LDW.

Based on this relationship, the image-derived features can estimate the growth-related traits, thus achieving nondestructive growth monitoring. For example, Chen et al. The authors extracted structure properties, color-related features, near-infrared NIR signals, and fluorescence-based features from images. Based on the above features, they built multiple models, i. The results showed that the RF model was able to accurately estimate the biomass of barley and better quantify the relationship between image-based features and barley biomass than the other methods.

Tackenberg et al. Image features, such as the projected area PA and proportion of greenish pixels, were extracted, which were then fitted to the actual measured values of the aboveground fresh biomass, oven-dried biomass, and dry matter content by linear regression LR.

The results showed that all the determined coefficients of the constructed models were higher than 0. In addition, the green fraction and greener fraction were also extracted. The results showed that the image features based on color had strong correlations with growth-related traits. Fan et al. The results showed that the image features derived from segmented images yielded better accuracy than those from non-segmented images, with an R 2 value of 0. Liu and Pattey 13 extracted the vertical gap fraction from digital images captured from nadir to estimate the LAI of corn, soybean, and wheat.

Prior to the extraction of the canopy vertical gap fraction, the authors adopted the histogram-based threshold method to segment the green vegetative pixels. The results showed that the LAI estimated by the digital images before canopy closure was correlated with the field measurements. Sakamoto et al. Although computer vision-based methods for estimating growth-related traits have achieved promising results, they are subject to two issues. First, the methods are susceptible to noise.

Since the images are captured under field conditions, noise caused by uneven illumination and cluttered backgrounds is inevitable, which will affect image segmentation and feature extraction, thus potentially reducing the accuracySecond, the methods greatly rely on manually designed image features, which have large computational complexity.

Moreover, the generalization ability of the extracted low-level image features is poor 16 ,Therefore, a more feasible and robust approach should be explored. Convolutional neural networks CNNs , which is a state-of-the-art deep learning approach, can directly take images as input to automatically learn complex feature representations 18 ,With a sufficient amount of data, CNNs can achieve better precision than conventional methods 20 ,Therefore, CNNs have been used in a wide range of agricultural applications, such as weed and crop recognition 19 , 22 , 23 , plant disease diagnosis 24 , 25 , 26 , 27 , 28 , and plant organ detection and counting 21 ,However, despite its extensive use in classification tasks, CNNs have rarely been applied to regression applications, and there are few reports on how CNNs have been used for the estimation of growth-related traits of greenhouse lettuce.

Inspired by Ma et al. The objective of this study is to achieve accurate estimations of growth-related traits for greenhouse lettuce.

By following the proposed framework, including lettuce image preprocessing, image augmentation, and CNN construction, this study will investigate the potential of using CNNs with digital images to estimate the growth-related traits of greenhouse lettuce throughout the entire growing season, thus exploring a feasible and robust approach for growth monitoring. Three cultivars of greenhouse lettuce, i. During the experiment, natural light was used for illumination, and a nutrient solution was circulated twice a day.

The experiment was performed from April 22, , to June 1,Six shelves were adopted in the experiment. Each shelf had a size of 3. The number of plants for each lettuce cultivar was 96, which were sequentially labeled. Image collection was performed using a low-cost Kinect 2.

Finally, two image datasets were constructed, i. The number of digital images for Flandria, Tiberius, and Locarno was 96, 94 two plants did not survive , and 96, respectively, and the number of depth images for the three cultivars was the same. Figure 1 shows examples of the cropped digital images for the three cultivars. Prior to the construction of the CNN model, the original digital image dataset was divided into two datasets in a ratio of , i.

The two datasets both covered all three cultivars and sampling intervals. The test dataset contained 57 digital images. To enhance data diversity and prevent overfitting, a data augmentation method was used to enlarge the training dataset Fig. To adapt the CNN model to the changing illumination of the greenhouse, the images in the training dataset were converted to the HSV color space, and the brightness of the images was adjusted by changing the V channelThe brightness of the images was adjusted to 0.

In total, the training dataset was enlarged by 26 times, resulting in digital images. These measurements were conducted at an interval of seven days, specifically on April 29, May 6, May 13, May 20, May 27, May 31, and June 1 ofFor the first six measurements, ten plants of greenhouse lettuce were randomly sampled each time for each cultivar.

The measurements were obtained using a destructive sampling method. The sample was placed on a balance with a precision of 0. Lincoln, Nebraska, USA. For the last measurement, all the remaining lettuce plants were harvested, and the measurements were obtained by using the same method.

The architecture of the CNN model is shown in Fig. The CNN model consisted of five convolutional layers, four pooling layers, and one fully connected layer. The number of kernels in the five convolutional layers were 32, 64, , , andTo keep the size of the feature maps as an integer, zero-padding was employed in the second and third convolutional layers. The average pooling function was adopted in the pooling layers instead of the max pooling function. The number of hidden neurons in the fully connected layer was three, corresponding to the three outputs of the model, i.

Therefore, the CNN model could estimate the three growth-related traits simultaneously. Dropout was used, and the rate was 0. In this study, the CNN model used stochastic gradient descent to optimize the network weights.

The initial learning rate of the model was set to 0. The mini-batch size was set to , and the maximum number of epochs for training was set toTo evaluate the performance of the CNN model, tests were performed with the widely adopted estimation methods. Two shallow machine learning classifiers, i. Therefore, it was necessary to conduct image segmentation to extract the lettuce pixels, thus ensuring that the extracted features in the following step were presenting the lettuce plants.

For the digital images of the greenhouse lettuce, since the color contrast between the lettuce plant and the background was very obvious, image segmentation was achieved by using the adaptive threshold method for the color information. Some segmentation results are shown in Fig.

To build the shallow machine learning classifiers, feature extraction was performed on the segmented images of greenhouse lettuce. According to the characteristics of the three cultivars of greenhouse lettuce, low-level image features, including color, texture, and shape features, were extractedBased on the color components, the gray level co-occurrence matrix 37 was combined to extract the texture features.

The texture features included the contrast, correlation, energy, and homogeneity of the 15 color components. The shape features of the greenhouse lettuce that were extracted were area and perimeter in this study. The area was the area enclosed by the outline, and the perimeter was the total length of the blade outline. After extracting the image features, the Pearson coefficient was used to perform correlation analysis between the extracted features and the actual values of the LFW, LDW, and LA of greenhouse lettuce.

The features with relatively high correlation values were used to build the shallow machine learning classifiers. In addition to the above image features, structural features derived from the depth images, including H, PA, and digital volume V , were also used to estimate the growth-related traits of the greenhouse lettuce 8 , 38 , 39 ,Similar to the processing of digital images, image segmentation was also conducted on the depth images, which was achieved by the entropy rate superpixel segmentation method

Greenhouse Manager

Horticulture is, at the most basic level, the science or art of cultivating fruits, vegetables, flowers, or ornamental plants. The origin of the term lies in two Latin words: hortus meaning "garden" and cultus which means "tilling". Master Gardeners are well-versed in this field, but its full definition actually extends beyond what we would normally think of as gardening or agriculture. The corresponding adjective to this noun is "horticultural. Professor William L. George of the Florida Department of Agriculture breaks horticulture down into five distinct sub-fields:.

There are hundreds of career pathways. Here are some of them to get you started exploring. Click on your interest below to show a list of plant careers in that.

Design and Maintenance of Green House 3(2+1)

Skip to content. Scheduling is an important part of greenhouse crop production. Accurate schedules are required to grow plants to marketable size at the right time of year. Poor scheduling may cause growers to experience having small or non-flowering plants, having overgrown plants at the height of the season or empty benches with several weeks of selling season still ahead. There are many factors that can influence finish timing of bedding plants including the maturity of plugs and liners, growing conditions for the plugs and liners, average day-time temperature, photoperiod, use of plant growth regulators and finish container sizes. Chronological age calendar age vs. To successfully schedule a crop, a grower has to decide the week of the year that a crop will be marketed, and then work backwards to determine the date of seed sowing or planting depending on the crop. If seedlings, plugs or liners are purchased, then the dates of delivery and transplanting will need to be determined.

Proposed features/greenhouse horticulture

Facilities Management has secured the services of Utilities Management Corporation who will provide ongoing monitoring, diagnosing and the optimization of energy and water use, billing and operations at the Theodore Roosevelt Building. Greenhouse means a structure with the sides primarily made of a transparent material such as glass , perspex or plastic for the purpose of growing of plants or hastening growth of plants under controlled environmental conditions ;. Sample 1. Sample 2.

Thousands of students have passed through the old horticulture greenhouses on Wilson Road since they were built inIn , these greenhouses were torn down.


He is the most recognized legal expert on matters relating to Massachusetts ag. He is associated with the Farm Bureau and several other organizations and is available to the agricultural community for consult and hire. He can be reached atYou can reach her atUMass Center for Agriculture has some tax information resources for you, whether you are a new or established grower, including links to IRS information. There is also a general federal farm income tax fact sheet on the Penn State Extension website.


Collins Dictionary of Biology, 3rd ed. Hale, V. Saunders, J. MarghamMentioned in?

Ornamental Horticulture and Landscape Design Plant Nutrition and Fertilizers for Greenhouse Production (PB ), develops back-.

Display Garden

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The Meaning of "Horticulture"

In its broadest definition, protected cropping includes the use of greenhouses and glasshouses, shade houses, screen houses and crop top structures. Controlled environment horticulture CEH is the most modern and sophisticated form of protected cropping. CEH combines high technology greenhouses with hydroponic soil-less growing systems. CEH makes it possible to consistently and reliably control or manipulate the growing environment and effectively manage nutrition, pests and diseases in crops.

A greenhouse is a generic term referring to the use of a transparent or partially transparent material supported by a structure to enclose an area for the propagation and cultivation of plants.

Local Regulation of Agricultural and Horticultural Operations

These structures are subject to a special rule for recapture of the credit. See paragraph g of this section. For the relation of this section to section 48 a 1 B other tangible property and to sections and depreciation recapture , see paragraph h of this section. The provisions of section 48 a 1 D and this section apply to open taxable years ending after August 15,Under section 48 p 2 , a single purpose agricultural structure is any structure or enclosure that meets all of the following requirements:. See paragraphs b 3 and 4 of this section.

Growth monitoring of greenhouse lettuce based on a convolutional neural network

Jun Total Pages:The global greenhouse horticulture market size is predicted to reach over USD 50 billion byRising need for extending the cropping season in order to feed the growing population and protect them from adverse climatic conditions and pests has aggravated the demand for greenhouse horticulture.

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