Information Processing in Agriculture最新文献

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.

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.

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.

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.

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.

Apple picking robot is now being developed as an alternative to hand picking due to a great demand for labor during apple harvest season. Accurate detection and localization of target fruit is necessary for robotic apple picking. Detection accuracy has a great influence on localization results. Although current researches on detection and localization of apples using traditional image algorithms can obtain good results under laboratory conditions, it is difficult to accurately detect and locate objects in natural field with complex environments. With the rapid development of artificial intelligence, accuracy of apple detection based on deep learning has been significantly improved. Therefore, a deep learning-based method was developed to accurately detect and locate the position of fruit. For different localization methods, binocular localization is a widely used localization method for its bionic principle and lower equipment cost. Hence, this paper proposed an improved binocular localization method for apple based on fruit detection using deep learning. First, apples of binocular images were detected by Faster R-CNN. After that, a segmentation based on chromatic aberration and chromatic aberration ratio was applied to segment apple and background pixels in bounding box of detected fruit. Furthermore, template matching with parallel polar line constraint was used to match apples in left and right images. Finally, two feature points on apples were selected to directly calculate three dimensional coordinates of feature points with the binocular localization principle. In this study, Faster R-CNN achieved an AP of 88.12% with an average detection speed of 0.32 s for an image. Meanwhile, standard deviation and localization precision of depth of two feature points on each apple were calculated to evaluate localization. Results showed that the average standard deviation and the average localization precision of 76 groups of datasets were 0.51 cm and 99.64%, respectively. Results indicated that the proposed improved binocular localization method is promising for fruit localization.

The high similarity of shellfish images and unbalanced samples are key factors affecting the accuracy of shellfish recognition. This study proposes a new shellfish recognition method FL_Net based on a Convolutional Neural Network (CNN). We first establish the shellfish image (SI) dataset with 68 species and 93 574 images, and then propose a filter pruning and repairing model driven by an output entropy and orthogonality measurement for the recognition of shellfish with high similarity features to improve the feature expression ability of valid information. For the shellfish recognition with unbalanced samples, a hybrid loss function, including regularization term and focus loss term, is employed to reduce the weight of easily classified samples by controlling the shared weight of each sample species to the total loss. The experimental results show that the accuracy of shellfish recognition of the proposed method is 93.95%, 13.68% higher than the benchmark network (VGG16), and the accuracy of shellfish recognition is improved by 0.46%, 17.41%, 17.36%, 4.46%, 1.67%, and 1.03% respectively compared with AlexNet, GoogLeNet, ResNet50, SN_Net, MutualNet, and ResNeSt, which are used to verify the efficiency of the proposed method.

Egg geometrical measurement is important for the poultry industry, and its calculation is not easily possible due to the unusual shape of the egg. To solve this problem a research has been carried out using a digital image analysis (IA) system to render the precise measurements of several egg size parameters, including egg volume (V) and surface area (S) of laying hen. We tested the accuracy of the IA method in determining egg physical properties by comparing the V resulting from IA with that measured using water displacement. The correlation of determination (R²) between the data obtained from these two methods was 0.98. We also applied the data sets of egg samples obtained by the IA to test the accuracy of the previously published equations to predict S and V in the egg samples. The results have shown that the equations posted by Carter (1975), Paganelli et al. (1974), and Narushin (1997) provided reasonable accuracy (R² > 0.839) in predicting the egg S based on the length (L) and maximum breadth (B). In addition, the equations proposed by Carter (1975), Ayupov (1976), and Narushin (1994, 1997, 2005) provided accurate predictions for egg V by using L and B as the inputs. Furthermore, multiple linear regression (MLR), polynomial regression (PR), and artificial neural networks (ANN) models were used to test whether we could find new simple equations to predict the egg volume and surface area based on the egg weight, L, and B. The results indicated that weight could not be a helpful input variable, while weight is the single input of most published equations. Our newly developed models are also accurate for predicting V and S of egg samples based on L and B.