Planting in Metallized Reflective Mulch Did Not Significantly Benefit Development of New Hybrid Citrus Seedlings

The U.S. Department of Agriculture citrus scion breeding program is urgently working on developing huanglongbing (HLB; pathogen Candidatus Liberibacter asiaticus)-tolerant cultivars with excellent fruit quality and productivity when HLB-affected. The slow process of assessing new citrus hybrids is a major impediment to delivery of these much-needed cultivars. We generate thousands of hybrids each year, germinate the seedlings, grow them for 2 years in the greenhouse, plant them at high density in a field where the disease HLB is abundant, grow them for 5 to 10 years, and make selections based on tree performance and fruit quality of these HLB-affected trees. Based on promising reports of accelerated citrus growth when grown in a metallized reflective mulch (MRM) system, we tested the hypothesis that the MRM system may accelerate growth and selection of new hybrid seedlings compared with conventional soil culture (CSC). In the MRM system, tree rows are covered with a layer of metallized plastic film and drip irrigation is installed beneath the plastic. In 2 years of analysis, tree canopy volume was significantly greater with MRM in 2020 (27% greater than CSC) but not in 2021, and MRM tree height was greater in 2021 (7% greater than CSC). Mortality was significantly greater with MRM in both 2020 and 2021(in 2021: 32% vs. 17% under CSC), and MRM trees had more chlorotic leaves. Because of staff limitations, plant debris and soil were not routinely cleared from MRM, thus diminishing any benefit from the reflective surface. Better maintenance might have resulted in more sustained evidence of MRM growth benefits. With the current resource availability, the MRM system does not appear to accelerate the assessment of hybrid seedling trees.

CONSTRUCTIVE AND TECHNOLOGICAL FEATURES OF MODERN BLOCK GREENHOUSES

Problem statement. The beginning of the production of vegetables in protected soil on an industrial basis in Ukraine was marked by the construction in the 80s of the last century of greenhouses from the structures of the Antratsyt Luhansk region prefabricated greenhouse plant. A new impetus to the development of greenhouse vegetable growing was provided by the commissioning of energy-saving modern winter greenhouses built in the period from 2005 to 2015. Greenhouse vegetable growing is not standing still, but is actively expanding around the world. Further development of this area is possible with the introduction of new technologies for construction and operation of greenhouses, as well as their design solutions. The development of greenhouse vegetable growing is an important economic task. The design and technological features of modern greenhouses of the fifth generation of the semi-closed type are shown, which allow to provide high yields at lower consumption of material resources. A study of the complexity of the construction of greenhouses, identified low-mechanized, manual processes. The purpose of the article is to show ways to improve the design and technological parameters of modern greenhouses. In order to ensure energy savings and increase yields. Results. The analysis of normative documents on design and construction of greenhouses is performed. It is shown that greenhouses, the so-called "semi-closed type", allow to obtain high vegetable yields and energy savings. The most mechanized processes for the construction of greenhouses are earthworks and foundations. Much of the manual labor takes place during the installation of the metal frame, glazing and heating systems. Scientific novelty and practical significance. For the first time the value of labor intensity and duration during the construction of greenhouses was obtained, their dependences on the main factors influencing the performance of construction and installation works were established. This allows at the stage of development of design and technological documentation to determine rational ways of performing work.

Hygienic Assessment Of The Impact Of Adverse Factors On The State Of Operating Greenhouse Farms

Currently, along with the expansion of protected ground areas, the intensification of greenhouse crop production is carried out, providing for a higher organizational and technological level, providing effective ways to accelerate the growth and development of plants, as well as protect them from numerous pests and diseases. In solving the problems of supplying the country's population with fresh vegetables throughout the year, providing flower and ornamental plants, as well as carrying out year-round plant breeding, an important role should be played by greenhouse plant growing [5, 7, 11].

Unsupervised Greenhouse Tomato Plant Segmentation Based on Self-Adaptive Iterative Latent Dirichlet Allocation from Surveillance Camera

It has long been a great concern in deep learning that we lack massive data for high-precision training sets, especially in the agriculture field. Plants in images captured in greenhouses, from a distance or up close, not only have various morphological structures but also can have a busy background, leading to huge challenges in labeling and segmentation. This article proposes an unsupervised statistical algorithm SAI-LDA (self-adaptive iterative latent Dirichlet allocation) to segment greenhouse tomato images from a field surveillance camera automatically, borrowing the language model LDA. Hierarchical wavelet features with an overlapping grid word document design and a modified density-based method quick-shift are adopted, respectively, according to different kinds of images, which are classified by specific proportions between fruits, leaves, and the background. We also utilize the feature correlation between several layers of the image to make further optimization through three rounds of iteration of LDA, with updated documents to achieve finer segmentation. Experiment results show that our method can automatically label the organs of the greenhouse plant under complex circumstances, fast and precisely, overcoming the difficulty of inferior real-time image quality caused by a surveillance camera, and thus obtain large amounts of valuable training sets.

Unsupervised segmentation of greenhouse plant images based on modified Latent Dirichlet Allocation

Agricultural greenhouse plant images with complicated scenes are difficult to precisely manually label. The appearance of leaf disease spots and mosses increases the difficulty in plant segmentation. Considering these problems, this paper proposed a statistical image segmentation algorithm MSBS-LDA (Mean-shift Bandwidths Searching Latent Dirichlet Allocation), which can perform unsupervised segmentation of greenhouse plants. The main idea of the algorithm is to take advantage of the language model LDA (Latent Dirichlet Allocation) to deal with image segmentation based on the design of spatial documents. The maximum points of probability density function in image space are mapped as documents and Mean-shift is utilized to fulfill the word-document assignment. The proportion of the first major word in word frequency statistics determines the coordinate space bandwidth, and the spatial LDA segmentation procedure iteratively searches for optimal color space bandwidth in the light of the LUV distances between classes. In view of the fruits in plant segmentation result and the ever-changing illumination condition in greenhouses, an improved leaf segmentation method based on watershed is proposed to further segment the leaves. Experiment results show that the proposed methods can segment greenhouse plants and leaves in an unsupervised way and obtain a high segmentation accuracy together with an effective extraction of the fruit part.