Information Processing in Agriculture最新文献

Portable measurement of ammonia nitrogen in water is of great significance for water quality monitoring. It’s beneficial to reduce biological diseases and promote aquatic product safety. Traditional methods such as Nessler’s reagent method suffer from complex operation, time delays and toxic residues. To realize simple and pollution-free detection, this paper develops a low-cost portable device for ammonia nitrogen detection. A test paper was proposed to cooperate the device and offer a chromogenic reaction. The portable device reduces the impact of any ambient light, simplifies the operation, and provides human–computer interaction. The result obtained for the detection range of 0.4–10 mg/L (R² are 0.990 2 and 0.989 3 for the rang of 0.4–4.5 and 4.5–10 mg/L, respectively) with the detection limit of 0.36 mg/L, and the average recovery of aquaculture water is 100.98–137.75%. The results show that the portable device can provide a great potential for on-site detection ammonia nitrogen concentration.

Agricultural greenhouse production has to require a stable and acceptable environment, it is therefore essential for future greenhouse production to obtain full and precisely internal dynamic environment parameters. Dynamic modeling based on machine learning methods, e.g., intelligent time series prediction modeling, is a popular and suitable way to solve the above issue. In this article, a systematic literature review on applying advanced time series models has been systematically conducted via a detailed analysis and evaluation of 61 pieces selected from 221 articles. The historical process of time series model application from the use of data and information strategies was first discussed. Subsequently, the accuracy and generalization of the model from the selection of model parameters and time steps, providing a new perspective for model development in this field, were compared and analyzed. Finally, the systematic review results demonstrate that, compared with traditional models, deep neural networks could increase data structure mining capabilities and overall information simulation capabilities through innovative and effective structures, thereby it could also broaden the selection range of environmental parameters for agricultural facilities and achieve environmental prediction end-to-end optimization via intelligent time series model based on deep neural networks.

An accurate environment model is a fundamental issue in greenhouses control to improve the energy consumption efficiency and to increase the crop yield. With the increase of agricultural data generated by the Internet of Things (IoT), more feasible models are necessary to get full usage of such information. In this research, a Gradient Boost Decision Tree (GBDT) model based on the newly-developed Light Gradient Boosting Machine algorithm (LightGBM or LGBM) was proposed to model the internal temperature of a greenhouse. Features including climate variables, control variables and additional temporal information collected within five years were used to construct a suitable dataset to train and validate the LGBM model. An adaptive cross-validation method was developed as a novelty to improve the LGBM model performance and self-adaptive ability. For comparison of the predictive accuracy, a Back-Propagation (BP) Neural Network model and a Recurrent Neural Network (RNN) model were built under the same process. Another two GBDT algorithms, Extreme Gradient Boosting (Xgboost) and Stochastic Gradient Boosting (SGB), were also introduced to compare the predictive accuracy with LGBM model. Results suggest that the LGBM has best fitting ability for the temperature curves with RMSE value at 0.645℃, as well as the fastest training speed among all algorithms with 60 times faster than the other two neural network algorithms. The LGBM has strongly potential application prospect on both greenhouse environment prediction and real-time predictive control.

On the current breeding goose farm, the detection of individual egg laying mainly depends on some judgement experiences of farm workers. At present, there have been some egg laying detection systems developed with images and weighing sensors, which only signal the eggs being laid, but no egg position being achieved. Meanwhile, the detection rate of the system is not high due to environment limitations like dim light of the goose barn. Therefore, to solve these problems mentioned above, an intelligent detection and positioning system is proposed by integrating technologies of the Radio Frequency (RF) and photoelectric sensors, together with the geometric calculation principle. In this research, individual egg laying information of breeding geese in a non-cage state was examined to improve the level of automatic detection and positioning in the field of breeder egg production. The results showed that an accurate detection and positioning of an egg in a nest filled with the artificial turf could be achieved under some conditions: the height of sensor is 3.5 cm from the bottom plate of the egg laying nest, the spacing of the photoresistor module is 5 cm, and the external light intensity is less than 110 LUX. It also shown that the error of the goose egg position recognition is 0.443 cm with a suitable level of straw in the nest. Therefore, the monitoring system and positioning method that was developed in this research could provide a reference for the analysis of individual egg laying behavior, and could result in an improvement in the automatic egg collection for the breeding geese production.

The normal growth of fishes is closely relevant to the density of mariculture. It is of great significance to accurately calculate the breeding area of specific sea area from satellite remote sensing images. However, there are no reports about cage segmentation and density detection based on remote sensing images so far. And the accurate segmentation of cages faces challenges from very large high-resolution images. Firstly, a new public mariculture cage data set is built. Secondly, the training set is augmented via sample variations to improve the robustness of the model. Then, for cage segmentation and density statistics, a new methodology based on Mask R-CNN is proposed. Using dividing and stitching technologies, the entire remote sensing test images of the cage can be accurately segmented. Finally, using the trained model, the object detection features and segmentation characteristics can be obtained at the same time. Considering only the area within the target detection frame, the proposed method can count the pixels in the segmented area, which can obtain accurate area and density while reducing time-consuming. Experimental results demonstrate that, compared with traditional contour extraction method and U-Net based scheme, the proposed scheme can significantly improve segmentation precision and model’s robustness. The relative error of the actual area is only 1.3%.

In precision agriculture, the accurate segmentation of crops and weeds in agronomic images has always been the center of attention. Many methods have been proposed but still the clean and sharp segmentation of crops and weeds is a challenging issue for the images with a high presence of weeds. This work proposes a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmentation of crops and weeds in color images. Agronomic images of two different databases were used for the segmentation algorithms. Using the thresholding technique, everything except plants was removed from the images. Afterward, semantic segmentation was applied using U-net followed by the segmentation of crops and weeds using the K-means subtractive algorithm. The comparison of segmentation performance was made for the proposed method and K-Means clustering and superpixels algorithms. The proposed algorithm provided more accurate segmentation in comparison to other methods with the maximum accuracy of equivalent to 99.19%. Based on the confusion matrix, the true-positive and true-negative values were 0.995 2 and 0.898 5 representing the true classification rate of crops and weeds, respectively. The results indicated that the proposed method successfully provided accurate and convincing results for the segmentation of crops and weeds in the images with a complex presence of weeds.

Agricultural crop production is a major contributing element to any country’s economy. To maintain the economic growth of any country plants disease detection is a leading factor in agriculture. The contribution of the proposed algorithm is to optimize the extracted information from the available resources for the betterment of the result without any additional complexity. The proposed technique basically localizes the leaf region prior to the image classification into healthy and diseased. The novelty of this work is to fuse the information extracted from the available resources and optimize it to enhance the expected outcome. The leaf colors are analyzed using color transformation for the seed region identification. The mapping of a low-dimensional RGB color image into L*a*b color space provides an expansion of the spectral range. The neighboring pixels-based leaf region growing is applied on the initial seeds. In order to refine the leaf boundary and the disease-affected areas, we employed a random sample consensus (RANSAC) for suitable curve fitting. The feature sets using bag of visual words, Fisher vectors, and handcrafted features are extracted followed by classification using logistic regression, multilayer perceptron model, and support vector machine. The performance of the proposal is analyzed through PlantVillage datasets of apple, bell pepper, cherry, corn, grape, potato, and tomato. The simulation-based analysis of the proposed contextualization-based image categorization process outperforms as compared with the state of arts. The proposed approach provides average accuracy and area under the curve of 0.932 and 0.903, respectively.

The increased demand for food worldwide, the reduced land availability for livestock production, the increasing cost of animal feed and the need for mitigating livestock-related greenhouse gas emissions have driven the search for animal feeding systems that proves more efficient. To tackle this problem, we propose the use of computational support to help researchers compare data on feed efficiency, therefore improving economic and environmental gains. As a solution, we present an integrative architecture capable of combining heterogeneous data from multiple experiments related to dairy cattle feed efficiency indices. The proposed architecture, called FeedEfficiencyService, classifies animals according to feed efficiency indices and allows visualizations through ontologies and inference engines. The results obtained from a case study with researchers from the Brazilian Agricultural Research Corporation – Dairy Cattle (EMBRAPA) demonstrate that this architecture is a supporting tool in their daily work routine. The researchers highlighted the importance of the proposed architecture as it allows analyzing animal data, comparing experiments, having reliable data analyses, and standardizing and organizing data from experiments. The novelty of our approach is the use of ontologies and inference engines to enable the discovery of new knowledge and new relationships between data from feed efficiency-related experiments. We store such data, relationships, and analyses of results in an integrated repository. This solution ensures unified access to the processing history and data from diverse experiments, including those conducted at external research centers.

Apparent electrical conductivity is an important parameter for describing the spatial variability of physical and chemical attributes of the soil and for the delineation of management zones. The objective of this work is to outline management zones for the cocoa cultivation based on the spatial variability of the productivity and the apparent electrical conductivity (ECa) of the soil. Data collection was performed in a regular sample grid containing 120 points in an area cultivated with cocoa trees, located in the municipality of Ilhéus, state of Bahia, Brazil. At each sampling point (cocoa tree), soil samples were collected to determine chemical attributes. Productivity was measured for one year, counting, monthly, the number of fruits, which were classified into off-season cocoa, harvest and annual production. Measurements of the apparent electrical conductivity of the soil were performed at different times of the year using a portable conductivity meter. The data were analyzed using classical statistics and geostatistics. The management zones were delineated using the fuzzy k-means algorithm. The ideal number of class was defined using the fuzziness performance index (FPI) and the entropy of the modified partition (MPE) indexes. The Kappa coefficient was used to validate the management zones, assessing their agreement with the chemical attributes of the soil. The ECa of the soil values presented moderate temporal variation, with maximum amplitude of 19.37 mS m⁻¹ and minimum of 0.82 mS m⁻¹ between measurement periods; higher averages of the ECa coincided with the highest levels of water in the soil. The measurements of the ECa of the soil carried out in April and October showed greater correlation with the chemical attributes of the soil, with significant values for 11 and 8 of the 17 attributes evaluated, respectively. The management zones from the ECa measured in April showed: a) reduced number of classes; b) spatial continuity between classes, and; c) agreement from reasonable (kappa between 0.20 and 0.40) to good (kappa > 0.41) with most of the chemical attributes of the soil. The ECa of the soil measured in April is, individually, the variable recommended for the management of soil fertility in tropical areas cultivated with cocoa trees.

The automatic classification of apple tree organs is of great significance for automatic pruning of apple trees, automatic picking of apple fruits, and estimation of fruit yield. However, there are some problems of dense foliage, partial occlusion and clustering of apple fruits. All of the problems above would contribute to the difficulties of organs classification and yield estimation of the apple trees. In this paper a method based on Color and Shape Multi-features Fusion and Support Vector Machine (SVM) for 3D apple tree organs classification and yield estimation was proposed. The method was designed for dwarf and densely planted apple trees at the early and late maturity stages. 196-dimensional feature vectors composed with Red Green Blue (RGB), Hue Saturation Value (HSV), Curvatures, Fast Point Feature Histogram (FPFH), and Spin Image were extracted firstly. And then the SVM based on linear kernel function was trained, after that the trained SVM was used for apple tree organs classification. Then the position weighted smoothing algorithm was used for classified apple tree organs smoothing. Then the agglomerative hierarchical clustering algorithm was used to recognize single apple fruit for yield estimation. On the same training and test set the experimental results showed that the SVM based on linear kernel function outperformed the KNN algorithm and Ensemble algorithm. The Recall, Precision and F1 score of the proposed method for yield estimation were 93.75%, 96.15% and 94.93% respectively. In summary, to solve the problems of apple tree organs classification and yield estimation in natural apple orchard, a novelty method based on multi-features fusion and SVM was proposed and achieve good performance. Moreover, the proposed method could provide technical support for automatic apple picking, automatic pruning of fruit trees, and automatic information acquisition and management in orchards.