Information Processing in Agriculture最新文献

Forest production and growth are obtained from statistical models that allow the generation of information at the tree or forest stand level. Although the use of regression models is common in forest measurement, there is a constant search for estimation procedures that provide greater accuracy. Recently, machine learning techniques have been used with satisfactory performance in measuring forests. However, methods such as Adaptive Neuro-Fuzzy Inference System (ANFIS) and Random Forest are relatively poorly studied for predicting the volume of wood in eucalyptus plantations in Brazil. Therefore, it is essential to check whether these techniques can provide gains in terms of accuracy. Thus, this study aimed to evaluate the use of Random Forest and ANFIS techniques in the prognosis of forest production. The data used come from continuous forest inventories carried out in stands of eucalyptus clones. The data were divided into 70% for training and 30% for validation. The algorithms used to generate rules in ANFIS were Subtractive Clustering and Fuzzy-C-Means. Besides, training was done with the hybrid algorithm (descending gradient and least squares) with the number of seasons ranging from 1 to 20. Several RFs were trained, varying the number of trees from 50 to 850 and the number of observations by five leaves to 35. Artificial neural networks and decision trees were also trained to compare the feasibility of the techniques. The evaluation of the estimates generated by the techniques for training and validation was calculated based on the following statistics: correlation coefficient (r), relative Bias (RB), and the relative root mean square error (RRMSE) in percentage. In general, the techniques studied in this work showed excellent performance for the training and validation data set with RRMSE values <6%, RB < 0.5%, and r > 0.98. The RF presented inferior statistics about the ANFIS for the prognosis of forest production. The Subtractive Clustering (SC) and Fuzzy-C-Means (FCM) algorithms provide accurate baseline and volume projection estimates; both techniques are good alternatives for selecting variables used in modeling forest production.

Tree fruit architecture results from combination of the training system and pruning and thinning processes across multiple growth and development years. Further, the tree fruit architecture contributes to the light interception and improves tree growth, fruit quality, and fruit yield, in addition to easing the process of orchard management and harvest. Currently tree architectural traits are measured manually by researchers or growers, which is labor-intensive and time-consuming. In this study, the remote sensing techniques were evaluated to phenotype critical architectural traits with the final goal to assist tree fruit breeders, physiologists and growers in collecting architectural traits efficiently and in a standardized manner. For this, a consumer-grade red–green–blue (RGB) camera was used to collect apple tree side-images, while an unmanned aerial vehicle (UAV) integrated RGB camera was programmed to image tree canopy at 15 m above ground level to evaluate multiple tree fruit architectures. The sensing data were compared to ground reference data associated with tree orchard blocks within three training systems (Spindle, V-trellis, Bi-axis), two rootstocks (‘WA 38 trees grafted on G41 and M9-Nic29) and two pruning methods (referred as bending and click pruning). The data were processed to extract architectural features from ground-based 2D images and UAV-based 3D digital surface model. The traits extracted from sensing data included box-counting fractal dimension (D_Bs), middle branch angle, number of branches, trunk basal diameter, and tree row volume (TRV). The results from ground-based sensing data indicated that there was a significant (P < 0.0001) difference in D_Bs between Spindle and V-trellis training systems, and correlations between D_Bs with tree height (r = 0.79) and total fruit yield per unit area (r = 0.74) were significant (P < 0.05). Moreover, correlations between average or total TRV and ground reference data, such as tree height and total fruit yield per unit area, were significant (P < 0.05). With the reported findings, this study demonstrated the potential of sensing techniques for phenotyping tree fruit architectural traits.

Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques. Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field. Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red, green and blue (RGB) imaging, thermal imaging, chlorophyll fluorescence imaging (CFIM), hyperspectral imaging, 3-dimensional (3-D) imaging or high resolution volumetric imaging. This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping. This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification. In this paper, information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods. This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural (2-D and 3-D), physiological and temporal trait estimation, and classification studies in plants.

Weeds that grow among crops are undesirable plants and have adversely affected crop growth and yield. Therefore, the study explores corn identification and positioning methods based on machine vision. The ultra-green feature algorithm and maximum between-class variance method (OTSU) were used to segment maize corn, weeds, and land; the segmentation effect was significant and can meet the following shape feature extraction requirements. Finally, the identification and positioning of corn were achieved by morphological reconstruction and pixel projection histogram method. The experiment reveals that when a weeding robot travels at a speed of 1.6 km/h, the recognition accuracy can reach 94.1%. The technique used in this study is accessible for normal cases and can make a good recognition effect; the accuracy and real-time requirements of robot recognition are improved and reduced the calculation time.

This study aimed to develop and evaluate the performance of a service system platform based on the Internet of Things (IoT) for monitoring nutritional deficiencies in plants and providing fertilizer recommendations. There are two distinct differences between this work and previous ones; namely, this service system platform has been developed based on IoT using a system engineering approach and its performance has been evaluated using dependability. We have successfully developed and integrated a service system platform and chlorophyll meter that is based on IoT. We have also successfully tested the performance of the service system platform using the JMeter software. The dependability value measured from the five tested variables (reliability, availability, integrity, maintainability, and safety) showed a value of 0.97 which represents a very good level of system confidence in not failing to deliver services to users under normal operational conditions. From a future perspective, this platform can be used as an alternative service to monitor nutrient deficiencies in plants and provide fertilization recommendations to increase yields, reduce fertilizer costs, and prevent the use of excessive fertilizers, which can cause environmental pollution.

Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting. Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce. This study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models, i.e. Multiple Linear Regression (MLR), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). One-way analysis of variance was applied to reduce RGB, HSV, and L*a*b* features number of hydroponic lettuce images. Image binarization, image mask, and image filling methods were employed to segment hydroponic lettuce from an image for models testing. Results showed that G, H, and a* were selected from RGB, HSV, and L*a*b* for training models. It took about 20.25 s to detect an image with 3 024 × 4 032 pixels by KNN, which was much longer than MLR (0.61 s) and SVM (1.98 s). MLR got detection accuracies of 89.48% and 99.29% for yellow and rotten leaves, respectively, while SVM reached 98.33% and 97.91%, respectively. SVM was more robust than MLR in detecting yellow and rotten leaves of hydroponic. Thus, it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.