Machine Vision Systems in Precision Agriculture for Crop Farming

Mavridou, Efthimia; Vrochidou, Εleni; Papakostas, George A.; Pachidis, Theodore; Kaburlasos, Vassilis G.

doi:10.3390/jimaging5120089

Cited by 208 publications

(113 citation statements)

References 100 publications

(231 reference statements)

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“…Over the last 40 years, a lot of research effort has been expended on developing harvesting robots for fruits and tomatoes [5][6][7][8][9][10][11][12][13]. Mavridou et al [14] presented a review of machine vision techniques in agriculture-related tasks focusing on crop farming. In [15], Schillaci et al attempted to solve the problem of recognizing mature greenhouse tomatoes using an SVM (support vector machine) classifier; however, the results of this work were not quantified.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Tomato and Peduncle Location System Based on Computer Vision for Use in Robotized Harvesting

et al. 2020

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Protected agriculture is a field in which the use of automatic systems is a key factor. In fact, the automatic harvesting of delicate fruit has not yet been perfected. This issue has received a great deal of attention over the last forty years, although no commercial harvesting robots are available at present, mainly due to the complexity and variability of the working environments. In this work we developed a computer vision system (CVS) to automate the detection and localization of fruit in a tomato crop in a typical Mediterranean greenhouse. The tasks to be performed by the system are: (1) the detection of the ripe tomatoes, (2) the location of the ripe tomatoes in the XY coordinates of the image, and (3) the location of the ripe tomatoes’ peduncles in the XY coordinates of the image. Tasks 1 and 2 were performed using a large set of digital image processing tools (enhancement, edge detection, segmentation, and the feature’s description of the tomatoes). Task 3 was carried out using basic trigonometry and numerical and geometrical descriptors. The results are very promising for beef and cluster tomatoes, with the system being able to classify 80.8% and 87.5%, respectively, of fruit with visible peduncles as “collectible”. The average processing time per image for visible ripe and harvested tomatoes was less than 30 ms.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic Tomato and Peduncle Location System Based on Computer Vision for Use in Robotized Harvesting

et al. 2020

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“…In certain situations (Figure 4), it is preferable to consider the negative values and then use the LeakyReLU (LR) variant that lets the negative fraction of the input pass (2).…”

Section: Gan Networkmentioning

confidence: 99%

“…Both G and D are based on typical layers, as presented in Table 2. ReLU (R) is a function of activation of a neuron that implements the mathematical function (1): In certain situations ( Figure 4), it is preferable to consider the negative values and then use the LeakyReLU (LR) variant that lets the negative fraction of the input pass (2).…”

Section: Gan Networkmentioning

confidence: 99%

“…is a difficult task in many remote image processing applications. Recently, considerable efforts have been made in this direction with applications in different domains like agriculture [1,2], environment [3,4], and transport [5,6]. On the other hand, the utility of the surveillance/monitoring systems on various areas has been proven by the management of natural disasters [7] and rescue activities.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation of Vegetation and Flood from Aerial Images Based on Decision Fusion of Neural Networks

Ichim

Popescu

2020

Remote Sensing

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The detection and evaluation of flood damage in rural zones are of great importance for farmers, local authorities, and insurance companies. To this end, the paper proposes an efficient system based on five neural networks to assess the degree of flooding and the remaining vegetation. After a previous analysis the following neural networks were selected as primary classifiers: you only look once network (YOLO), generative adversarial network (GAN), AlexNet, LeNet, and residual network (ResNet). Their outputs were connected in a decision fusion scheme, as a new convolutional layer, considering two sets of components: (a) the weights, corresponding to the proven accuracy of the primary neural networks in the validation phase, and (b) the probabilities generated by the neural networks as primary classification results in the operational (testing) phase. Thus, a subjective behavior (individual interpretation of single neural networks) was transformed into a more objective behavior (interpretation based on fusion of information). The images, difficult to be segmented, were obtained from an unmanned aerial vehicle photogrammetry flight after a moderate flood in a rural region of Romania and make up our database. For segmentation and evaluation of the flooded zones and vegetation, the images were first decomposed in patches and, after classification the resulting marked patches were re-composed in segmented images. From the performance analysis point of view, better results were obtained with the proposed system than the neural networks taken separately and with respect to some works from the references.

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“…In (20) , the author presents set of route map towards crop farming. The article studies set of methods towards fruit grading, counting, estimating the yield, and so on, also, the article focused on monitoring the health of plants towards weed, disease and insects.…”

Section: Introductionmentioning

confidence: 99%

Real time satellite image based CCF approximation model for efficient sugarcane growth and yield estimation using artificial neural networks

Saranya¹,

Thenmozhi²

2020

IJST

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Objective: To improve the performance of an efficient satellite image based CCF (Color, Climate, Flow) approximation model is presented in this article. Method: We attempted for plant growth estimation and yield estimation using artificial neural networks. The model receives the satellite images and preprocesses to improve the quality of the image. From the quality improved image, the method extracts the color values. Further, the features like climate and flow features from the data set of the region have been extracted. Using these features, for different time window, the method generates number of neurons and initializes them with the set of features. The set of images from the data set are used to extract several features and used to train the network. The classification is performed according to the same set of features obtained from input satellite image and the other features of the region at current window. The method estimates CCF plant growth and yield support measures as result. Based on these values, the yield estimation is performed. Result: The proposed method improves the performance of plant growth up to 97.25 and the yield estimation performance is increased up to 985 which is higher than previous approaches. Provide comparative estimation. Novelty: The proposed CCF model consider color, climate and features in plant growth estimation which differ from other approaches by considering maximum features obtained from satellite images as well as features collected from ground truth in different time stamp.

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Machine Vision Systems in Precision Agriculture for Crop Farming

Cited by 208 publications

References 100 publications

Automatic Tomato and Peduncle Location System Based on Computer Vision for Use in Robotized Harvesting

Automatic Tomato and Peduncle Location System Based on Computer Vision for Use in Robotized Harvesting

Segmentation of Vegetation and Flood from Aerial Images Based on Decision Fusion of Neural Networks

Real time satellite image based CCF approximation model for efficient sugarcane growth and yield estimation using artificial neural networks

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