Information Processing in Agriculture最新文献

Sugarcane planting is an important and growing activity in Brazil. Thereupon, several techniques have been developed over the years to maximize crop productivity and profit, amongst them, processing of sugarcane field images. In this sense, this research aims to identify and analyze crop rows and measure their gaps from aerial images of sugarcane fields. For this, a small Remotely Piloted Aircraft captured the images, generating orthomosaics of the areas for analysis. Then, each orthomosaic is classified with the K-Nearest Neighbor algorithm to segment regions of interest. Planting row orientation is estimated using the RGB gradient filter. Morphological operations and computational geometry models are then used to detect and map rows and gaps along the planting row segment. To evaluate the results, crop rows are mapped and compared to manually taken measurements. Our technique obtained an error smaller than 2% when compared to gap length in crop rows from an orthomosaic with the area of 8.05 ha (ha). The proposed approach can map the positioning of the automatically generated row segments appropriately onto manually created segments. Moreover, our method also achieved similar results when confronted with a manual technique for differing growth stages (40 and 80 days after harvest) of the sugarcane crop. The proposed method presents a great potential to be adopted in sugarcane planting monitoring.

Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production, starting from the upstream and downstream. This content can be used to monitor the progress of the oil palm fresh fruit bunch (FFB) and be applied to identify product profitability. Based on the near-infrared (NIR) signals, this study proposes an empirical mode decomposition (EMD) technique to decompose signals and predict the oil content of palm fruit. First, 350 palm fruits with Tenera varieties (Elaeis guineensis Jacq. var. tenera), at various ages of maturity, were harvested from the Cikabayan Oil Palm Plantation (IPB University, Indonesia). Second, each sample was sent directly to the laboratory for NIR signal measurements and oil content extraction. Then, the EMD analysis and artificial neural network (ANN) were employed to correlate the NIR signals and oil content. Finally, a robust EMD-ANN model is generated by optimizing the lowest possible errors. Based on performance evaluation, the proposed technique can predict oil content with a coefficient of determination (R²) of 0.933 ± 0.015 and a root mean squared error (RMSE) of 1.446 ± 0.208. These results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly, without neither solvents nor reagents, which makes it environmentally friendly. Therefore, the proposed technique has a promising potential to be applied in the oil palm industry. Measurements like this will lead to the effective and efficient management of oil palm production.

A large loss occurs in the combine harvesting of rapeseeds due to the fragility of rapeseed pods, and all the more so with the vibration of the combine header and the collision between the header and plants. Seed loss is greatly affected by the biomechanical properties of ready-to-harvest rapeseed plants. To understand the mechanism of pod cracking and seed loss and to propose measures for alleviating them, it is needed to study the biomechanical properties of ready-to-harvest rapeseed plants. To this end, “Huayouza 62”, a widely planted rapeseed variety in central China, was selected to study the biomechanical properties, including pod-cracking resistance, main stem-shearing resistance and resonant frequencies, of whole plants. The results showed that the distribution of pod-cracking resistance forces was 1.333–6.100 N in the mature stage, and the pod width and thickness had a significant influence on the cracking resistance. The main influencing factor of the main stem-shearing resistance was the stem diameter. A thicker main stem resulted in a larger shearing resistance force but a smaller shear stress. The moisture contents of the main stems varied from 47.71% to 76.13%. However, the varying moisture contents did not show a significant impact on the shearing resistance. The resonant frequencies of whole rapeseed plants ready for harvest ranged from 6.5 Hz to 7.5 Hz, which was close to the excitation frequency of the cutter bar on the 4LL-1.5Y harvester. This study lays a foundation for improving the design and construction of harvesting devices for rapeseed plants to reduce seed loss.

The objective of this study is to investigate the effectiveness of a multi-parameter intelligent fault diagnosis method based on a modified random forest algorithm (RFNB algorithm), so as to reduce the impact of blockage fault on the operation of a silage harvester, thus providing a reference for the intelligent control. In brief, the forward speed, cutting speed, engine speed and engine load were selected as the input variables. Then, a random forest (RF) was used to construct a naive Bayes classifier for each node of the decision tree, and finally the RFNB algorithm constituted based on the naive Bayes tree (NBTree). The results revealed that by improving the classification accuracy of a single decision tree, the fault diagnosis accuracy of the entire RF was improved. When the sample data were consistent, the accuracy of the RFNB algorithm was 97.9%, while that of the RF algorithm was only 93.27%. Besides, the performance of RFNB classifiers was significantly better than that of RF classifiers. In conclusion, the RFNB model can accurately identify the fault status of the silage harvester with its good robustness, which provides a new idea for the fault monitoring and early warning of large agricultural rotating machinery in the future.

In this study, the sensitivity of a novel dehumidification requirement model (DehumReq) is analyzed to evaluate the effect of the predominant factors on the dehumidification needs of the greenhouses. The hourly dehumidification demand and sensitivity coefficient (SC) are estimated for three different seasons: warm (July), mild (May), and cold (November), by using the local sensitivity analysis method. Based on SC values, the solar radiation, air exchange, leaf area index (LAI), and indoor setpoints (temperature, relative humidity (RH), and water vapor partial pressure (WVPP)) have significant impact on the dehumidification needs, and the impact varies from season to season. Most parameters have a higher SC in summer, whereas solar radiation and LAI have a higher SC in mild season. The dehumidification load increases 4 times of its base value with increasing solar radiation by 200 W/m², and the highest LAI (10) caused 5 times increment of the load. The changing of WVPP from its base value (1.5 kPa) to maximum (2.9 kPa) reduces the load 70% in summer. Air exchange was found to be the most crucial parameter because it is the main dehumidification approach that has a large range and is easily adjustable for any greenhouses. Sufficient air exchange by ventilation or infiltration will reduce the dehumidification load to zero in May and November and minimizes it to only nighttime load in July. For the other parameters, higher ambient air RH and indoor air speed will result in higher the dehumidification load; whereas higher inner surface condensation will result in lower dehumidification load. The result of this study will assist in the selection of the most efficient moisture control strategies and techniques for greenhouse humidity control.

The world receives more than 200 thousand people in a day and it is expected that the total world population will reach 9.6 billion by the year 2050. This will result in extra food demand, which can only be met from enhanced crop yield. Therefore, modernization of the agricultural sector becomes the need of the hour. There are many constraints that are responsible for the low production of crops, which can be overcome by using drone technology in the agriculture sector. This paper presents an analysis of drone technologies and their modifications with time in the agriculture sector in the last decade. The application of drones in the area of crop monitoring, and pesticide spraying for Precision Agriculture (PA) has been covered. The work done related to drone structure, multiple sensor development, innovation in spot area spraying has been presented. Moreover, the use of Artificial Intelligent (AI) and deep learning for the remote monitoring of crops has been discussed.

The accurate identification, detection, and segmentation of tea buds and leaves are important factors for realizing intelligent tea picking. A tea picking point location method based on the region-based convolutional neural network(R-CNN) Mask- RCNN is proposed, and a tea bud and leaf and picking point recognition model is established. First, tea buds and leaf pictures are collected in a complex environment, the Resnet50 residual network and a feature pyramid network (FPN) are used to extract bud and leaf features, and preliminary classification and preselection box regression training-performed on the feature maps through a regional proposal network (RPN). Second, the regional feature aggregation method (RoIAlign) is used to eliminate the quantization error, and the feature map of the preselected region of interest (ROI) is converted into a fixed-size feature map. The output module of the model realizes the functions of classification, regression and segmentation. Finally, through the output mask image and the positioning algorithm the positioning of the picking points of tea buds and leaves is determined. One hundred tea tree bud and leaf pictures in a complex environment are selected for testing. The experimental results show that the average detection accuracy rate reaches 93.95% and that the recall rate reaches 92.48%. The tea picking location method presented in this paper is more versatile and robust in complex environments.

Wheat ear counting is a prerequisite for the evaluation of wheat yield. A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation. The frequency domain decomposition of wheat ear image is completed by multiscale support value filter (MSVF) combined with improved sampled contourlet transform (ISCT). Support Vector Machine (SVM) is the classic classification and regression algorithm of machine learning. MSVF based on this has strong frequency domain filtering and generalization ability, which can effectively remove the complex background, while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat ears. In order to improve the level of wheat yield prediction, MSVF-ISCT method is used to decompose the ear image in multi-scale and multi direction in frequency domain, reduce the interference of irrelevant information, and generate the sub-band image with more abundant information components of ear feature information. Then, the ear feature is extracted by morphological operation and maximum entropy threshold segmentation, and the skeleton thinning and corner detection algorithms are used to count the results. The number of wheat ears in the image can be accurately counted. Experiments show that compared with the traditional algorithms based on spatial domain, this method significantly improves the accuracy of wheat ear counting, which can provide guidance and application for the field of agricultural precision yield estimation.

The present work describes the development of a computer vision system for the early detection of anthracnose in sugar mango based on Ultraviolet A illumination (UV-A). Anthracnose, a disease caused by the fungus Colletotrichum sp, is commonly found in the fruit of sugar mango (Mangifera indica). It manifests as surface defects including black spots and is responsible for reducing the quality of the fruit. Consequently, it decreases its commercial value. In more detail, this study poses a system that begins with image acquisition under white and ultraviolet illumination. Furthermore, it proposes to analyze the Red, Green and Blue color information (R, G, B) of the pixels under two types of illumination, using four different methods: RGB-threshold, RGB-Linear Discriminant Analysis (RGB-LDA), UV-LDA, and UV-threshold. This analysis produces an early semantic segmentation of healthy and diseased areas of the mango image. The results showed that the combination of the linear discriminant analysis (LDA) and UV-A light (called UV-LDA method) in sugar mango images allows early detection of anthracnose. Particularly, this method achieves the identification of the disease one day earlier than by an expert with respect to the scale of anthracnose severity implemented in this work.

Frost damage is one of the major concerns for crop growers as it can impact the growth of the plants and hence, yields. Early detection of frost can help farmers mitigating its impact. In the past, frost detection was a manual or visual process. Image-based techniques are increasingly being used to understand frost development in plants and automatic assessment of damage resulting from frost. This research presents a comprehensive survey of the state-of the-art methods applied to detect and analyse frost stress in plants. We identify three broad computational learning approaches i.e., statistical, traditional machine learning and deep learning, applied to images to detect and analyse frost in plants. We propose a novel taxonomy to classify the existing studies based on several attributes. This taxonomy has been developed to classify the major characteristics of a significant body of published research. In this survey, we profile 80 relevant papers based on the proposed taxonomy. We thoroughly analyse and discuss the techniques used in the various approaches, i.e., data acquisition, data preparation, feature extraction, computational learning, and evaluation. We summarise the current challenges and discuss the opportunities for future research and development in this area including in-field advanced artificial intelligence systems for real-time frost monitoring.