Image/video processing for fruit detection in the tree using hard-coded feature extraction algorithms has shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This architecture takes the input image and divides into AxA grid, where A is a configurable hyper-parameter that defines the fineness of the grid. To each grid cell an image detection and localization algorithm is applied. Each of those cells is responsible to predict bounding boxes and confidence score for fruit (apple and pear in the case of this study) detected in that cell. We want this confidence score to be high if a fruit exists in a cell, otherwise to be zero, if no fruit is in the cell. More than 100 images of apple and pear trees were taken. Each tree image with approximately 50 fruits, that at the end resulted on more than 5000 images of apple and pear fruits each. Labeling images for training consisted on manually specifying the bounding boxes for fruits, where (x, y) are the center coordinates of the box and (w, h) are width and height. This architecture showed an accuracy of more than 90% fruit detection. Based on correlation between number of visible fruits, detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Processing speed is higher than 20 FPS which is fast enough for any grasping/harvesting robotic arm or other real-time applications. HIGHLIGHTS Using new convolutional deep learning techniques based on single-shot detectors to detect and count fruits (apple and pear) within the tree canopy.
The novel coronavirus disease (COVID-19), originating from China, has rapidly crossed borders, infecting people worldwide. While its transmission may occur predominantly via aerosolization of virus-laden droplets, the possibility of other routes of contagion via the environment necessitates considerable scientific consideration. SARS-CoV-2 viral RNA has been detected in the feces of infected persons, and studies also have reported its occurrence in wastewater and surface water bodies. Therefore, water may be a possible route of virus outbreaks. Agricultural irrigation is the largest use of water globally, accounting for 70% of water use worldwide. Ensuring adequate water quality within irrigation practices is fundamental to prevent harm to plants and soils, maintain food safety, and protect public health. This review aims to gather information on possible SARS-CoV-2 transmission routes within urban and rural water environments, looking into the detection, persistence, and fate of SARS-CoV-2. Based on published literature, the effect of current treatment technologies in wastewater treatment plants (WWTPs) on SARS-CoV-2 inactivation has also been investigated. Preliminary research efforts that concentrated on SARS-CoV-2 indicate that the risk of virus transmission from the aquatic environment may currently be non-existent, although a few studies have reported the presence of SARS-CoV RNA in soils, whereas there are still no studies on the detection of SARS-CoV-2 in crops.
Rootstock vigor is well known to affect yield and productive performance in many fruit crops and the dwarfing trait is often the preferred choice for modern orchard systems thanks to its improved productivity and reduced canopy volume. This work investigates the different physiological responses induced by rootstock vigor on cherry, by comparing shoot and fruit growth, water relations, leaf gas exchanges as well as fruit vascular and transpiration in/outflows of "Black Star" trees grafted on semi-vigorous (CAB6P) and on semi-dwarfing (Gisela™6) rootstocks. The daily patterns of stem (stem), leaf (leaf) and fruit (fruit) water potential, leaf photosynthesis, stomatal conductance and transpiration, shoot and fruit growth, fruit phloem, xylem and transpiration flows were assessed both in pre-and post-veraison, while productivity and fruit quality were determined at harvest. At both stages, no significant differences were found on leaf , photosynthesis, fruit daily growth rates as well as fruit vascular and transpiration flows, while trees on Gisela™6 showed lower shoot growth rates and lower stem and fruit than trees on CAB6P. The resulting decrease in stem-to-leaf gradient on Gisela™6 trees determined a reduction in shoot growth by decreasing shoot strength as sinks for water and carbohydrates. On the other hand, Gisela™6 fruit lowered their fruit thanks to a higher osmotic accumulation and increased their competitiveness towards shoots, as confirmed by the higher productivity and fruit soluble solid content found at harvest for these trees. These results indicate that rootstock vigor alters resource competition between vegetative and reproductive growth, which can affect water use efficiency, yield, and fruit quality.
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