Aiming at the shortcoming of deep neural network in crop disease diagnosis, a lightweight compressed depth neural network for tomato disease diagnosis is proposed. Multi-scale convolution is used to increase receptive field, extract more abundant features, reduce model parameters and realize lightweight of the network by adopting the strategies of group convolution, depth separable convolution, pointwise convolution, channel shuffle, etc. For the lightweight model that has been initially trained, pruning operation is used to cut filter weight that is not important to reduce redundancy of the model. Experiments show that the accuracy of tomato disease diagnosis using the lightweight model is 98.61% after training only 10 epochs, it meets the needs of tomato disease diagnosis in agricultural production due to the small calculation and fast detection speed, and when cutting about 50% filter weight, the accuracy has only dropped 0.70%, which has a good effect.
The waste of fish food has always been a serious problem in aquaculture. On one hand, the leftover fish food spawns a big waste in the aquaculture industry because fish food accounts for a large proportion of the investment. On the other hand, the left over fish food may pollute the water and make fishes sick. In general, the reason for fish food waste is that there is no feedback about the consumption of delivered fish food after feeding. So it is extremely difficult for fish farmers to determine the amount of feedstuff that should be delivered each time and the feeding intervals. In this paper, we propose an effective method using image processing techniques to solve this problem. During feeding events, we use an underwater camera with supplementary LED lights to obtain images of uneaten fish food pellets on the tank bottom. An algorithm is then developed to figure out the number of left pellets using adaptive Otsu thresholding and a linear-time component labeling algorithm. This proposed algorithm proves to be effective in handling the non-uniform lighting and very accurate number of pellets are counted in experiments.
The digitization of greenhouse plants is an important aspect of digital agriculture. Its ultimate aim is to reconstruct a
visible and interoperable virtual plant model on the computer by using state-of-the-art image process and computer
graphics technologies. The most prominent difficulties of the digitization of greenhouse plants include how to acquire
the three-dimensional shape data of greenhouse plants and how to carry out its realistic stereo reconstruction. Concerning
these issues an effective method for the digitization of greenhouse plants is proposed by using a binocular stereo vision
system in this paper. Stereo vision is a technique aiming at inferring depth information from two or more cameras; it
consists of four parts: calibration of the cameras, stereo rectification, search of stereo correspondence and triangulation.
Through the final triangulation procedure, the 3D point cloud of the plant can be achieved. The proposed stereo vision
system can facilitate further segmentation of plant organs such as stems and leaves; moreover, it can provide reliable
digital samples for the visualization of greenhouse tomato plants.
In this paper, a three-dimensional reconstruction method, which is based on point clouds and texture images, is used to realize the visualization of leaves of greenhouse crops. We take Epipremnum aureum as the object for study and focus on applying the triangular meshing method to organize and categorize scattered point cloud input data of leaves, and then construct a triangulated surface with interconnection topology to simulate the real surface of the object. At last we texture-map the leaf surface with real images to present a life-like 3D model which can be used to simulate the growth of greenhouse plants.
The automatic segmentation and recognition of greenhouse crop is an important aspect in digitized facility
agriculture. Crop stems are closely related with the growth of the crop. Meanwhile, they are also an important
physiological trait to identify the species of plants. For these reasons, this paper focuses on the digitization process to
collect and analysis stems of greenhouse plants (tomatoes). An algorithm for automatic stem detection and extraction is
proposed, based on a cheap and effective stereo vision system—Kinect. In order to demonstrate the usefulness and the
potential applicability of our algorithm, a virtual tomato plant, whose stems are rendered by segmented stem texture
samples, is reconstructed on OpenGL graphic platform.
In this paper, a fast and effective 3D reconstruction method for the growth of greenhouse tomato plant is proposed by
using real organ samples and a parametric L-system. By analyzing the stereo structure of tomato plant, we extracts rules
and parameters to assemble an L-system that is able to simulate the plant growth, and then the components of the L-system
are translated into plant organ entities via image processing and computer graphics techniques. This method can
efficiently and faithfully simulate the growing process of the greenhouse tomato plant.
High-resolution Fourier transform spectra of 13CD3OH methanol have been re-examinedbetween 800 - 1350 cm-1. In addition to further fundamental understanding of its complex torsion- vibration energy structure, several new FIR laser assignments have been made, which involve IR pumptransitions to the v5, v7, and v 8 CD3-deformation, CO-stretching and CD3-rocking states. Theassignments have been used to confirm interesting perturbations in the energy level systems.
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