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[学术文献 ] 灌溉方式对石灰性褐土团聚体分布特征、稳定性及养分含量的影响 进入全文
中国生态农业学报(中英文)
为探明不同灌溉方式对石灰性褐土团聚体分布特征、稳定性及养分含量的影响， 于2016—2021年在山西农业大学小麦研究所韩村实验基地开展大田定位试验。设连续微喷灌（SI）、连续滴灌（DI）、连续漫灌（FI）和第1、2、5年漫灌与第3、4年微喷灌轮换（RI） 4个处理。定位5年后， 测定>0.25 mm团聚体重量百分含量(DR0.25、WR0.25)、平均重量直径（MWD）、几何平均直径（GMD）、破坏率（PAD）、分形维数（D）等土壤团聚体粒径分布、稳定性指标， 以及土壤水稳性团聚体有机碳、有效磷、速效钾含量等养分指标， 并对水稳性团聚体分布特征、稳定性及养分含量进行了相关分析。主要结果: 1)机械稳定性团聚体在0～10 cm土层SI、DI和RI处理均以0.5～1 mm为优势粒径（FI为<0.25 mm）， 在10～20 cm土层DI、FI和RI处理均以>5 mm为优势粒径（SI为0.5～1 mm）， 在20～50 cm土层各灌溉处理的优势粒径则均为>5 mm; 水稳性团聚体在0～50 cm土层4种灌溉处理均以<0.25 mm为优势粒径， 但FI处理的重量百分比最高。2) SI和DI处理在0～50 cm土层有效提高了WR0.25、降低了D， 且MWD、GMD总体上高于FI和RI处理， 并在30～50 cm土层显著降低了PAD， 而FI、RI处理的这些稳定性指标受土层深度影响较大。3)与其他2个处理相比， SI、DI处理在0～50 cm土层提高了土壤水稳性大团聚体（>0.25 mm）有机碳、有效磷、速效钾含量， 尤其在提高0～30 cm土层有效磷含量和30～50 cm土层速效钾含量上效果更为显著。4)相关性分析表明， 土层、WR0.25、MWD、GMD、PAD、D、水稳性大团聚体养分含量（有机碳、有效磷、速效钾）之间的相关性均达到显著（P<0.05）或极显著（P<0.01）水平。综上所述， 微喷灌、滴灌水肥一体化更有利于改善土壤结构和性状， 促进土壤大团聚体形成， 提高水稳性团聚体稳定水平及养分含量， 应值得推广应用。
[会议论文 ] Towards Hybrid Smart Irrigation for Mixed-Cropping 进入全文
IEEE
Agricultural countries are facing water shortage problems. Due to mismanagement of the water distribution system and selection of inappropriate irrigation methods, water could be over-utilized that will affect crop production. Consequently, the economy of these countries could be badly affected. Moreover, water shortage problems also occur and the agriculturists bear financial losses as a result of this water scarcity. Existing irrigation systems are designed for specific crops cultivated in a season but that is not useful for other crops planned to be grown in the next season. Currently, the practice of mixed cropping is being used in the same agricultural field to gain more income in which farmers are growing vegetables with regular crops. For this practice, computerized systems are required for water scheduling among both crops. To cope up with these challenges, several Irrigation systems i.e. using WSNs, cloud-based, rule mining based and mobile-oriented solutions using GPRS have been proposed in the literature. These systems are functioning on unidirectional data focusing on a single irrigation system at a time, hence are unable to address factual solutions. There is a fundamental need to develop smart contextaware irrigation systems with the ability to deal with multicropping/mixed cropping in a particular field. In this paper, a smart context-aware irrigation system has been presented to deploy automated hybrid irrigation methods for mixed cropping practices to achieve more yield production. The system entails three major parts: 1) WSNs based data acquirement systems, 2) Decision Supports System and 3) Context-aware water scheduling. This system will help farmers in intensive farming of mixed cropping and cultivate large areas with less amount of water by applying three standard methods of irrigation according to the type and requirements of crops, land condition/soil types with context-awareness.
[会议论文 ] A Deep Learning Approach to Mapping Irrigation Using Landsat: IrrMapper U-Net 进入全文
IEEE
Accurate maps of irrigation are essential for understanding and managing water resources. We present a new method of mapping irrigation based on an ensemble of convolutional neural networks that use reflectance information from Landsat imagery to classify irrigated pixels. The methodology does not rely on extensive feature engineering and does not condition the classification with land use information from existing geospatial datasets. The ensemble does not need exhaustive hyperparameter tuning and the analysis pipeline is lightweight enough to be implemented on a personal computer. Furthermore, the proposed methodology provides an estimate of the uncertainty associated with classification. We evaluated our methodology and the resulting irrigation maps using a highly accurate novel spatially-explicit ground truth data set, using county-scale USDA surveys of irrigation extent, and using cadastral surveys. We demonstrate the accuracy of the method by mapping irrigation over the state of Montana from years 2000- 2019. We found that our method outperforms other methods that use satellite remote sensing information in terms of overall accuracy and precision. We found that our method agrees better statewide with the USDA National Agricultural Statistics Survey estimates of irrigated area compared to other methods, and has far fewer errors of commission in rainfed agriculture areas. This methodology has the potential to be applied across the entire United States and for the complete Landsat record.
[会议论文 ] AN ENHANCED APPROACH FOR CROP YIELD PREDICTION SYSTEM USING LINEAR SUPPORT VECTOR MACHINE MODEL 进入全文
IEEE
Smart Agriculture is an emerging progressing field which is used for the management of farming to increase the yield of the crops. Since India is a populated country, urge of food production also increases. This situation is one of the reasons that hindering the development of country. At present farmers get more yield for their crop, but the market price for that crop is very less. To conquer these problems, a machine learning technology is used. The prediction will assist the farmers to select whether the specific crop is suitable for certain season and crop price values. Prediction techniques like linear regression, SVM, KNN method and decision tree of machine learning is widely used in the field of agriculture. This paper proposes a novel method that would deliver suitable support vectors for a SVM classification based on auxiliary information. This optimized method is applied to a real time agricultural application situation which utilize accuracy classification in turn aid production management. The proposed SVM method gives an accuracy of 91% than the existing system. This method can be implemented in several government sectors like APMC, kissan call centre etc., by which the government and farmers can get the information of the future crop yield and the market price.
[会议论文 ] A Time-Series Based Yield Forecasting Model Using Stacked Lstm To Predict The Yield Of Paddy In Cauvery Delta Zone In Tamilnadu 进入全文
IEEE
Cauvery delta zone in Tamilnadu is called as “Nerkazhanchiyam” (the land of Paddy) of the state, as it has the potential to produce paddy in huge quantity that can be suffice the need of the state. This zone includes the districts such as Thanjavur, Tiruvarur, Nagapattinam, Trichy and Cuddalore. These districts account for about 53% of production of paddy in the state. Increasing the production of paddy in Cauvery Delta Zone would satisfy the requirement of rice in the state on the whole. This will also have a substantial influence on both the farmer's and the nation's economy. Forecasting the production of crops beforehand could assist the farmers in improving their productivity. This necessitates the design of a precise crop yield prediction model. Crop production in agriculture is primarily determined by a variety of factors that falls under three categories: technological (agricultural techniques, managerial decisions, etc.), biological (diseases, insects, pests, etc.), and environmental (climate change, etc.). Among these factors environmental factors pose a great challenge to the decision makers in developing a precise prediction model. Hence, it is proposed to develop a suitable yield prediction model to predict the yield of paddy in Cauvery delta region considering the environmental factors along with the supplied nutrients. The proposed prediction model makes use of Long Short Term Memory (LSTM) algorithm which is a popular deep learning algorithm, to forecast the yield of paddy. LSTM is well known for its better prediction using time series data. Performance of the proposed prediction model is measured using the training loss and validation loss.
[会议论文 ] IoT based Smart Irrigation Module for Smart Cultivation 进入全文
IEEE
Majority of the ranchers utilize enormous parts of cultivating area and it turns out to be exceptionally hard to reach and track each edge of huge terrains. At some point, there is a chance of lopsided water monetary misfortunes. Smart Irrigation system modules utilizing the latest IoT based sensors with optimal communication will be very much useful for efficient cultivation. The Smart irrigation-based system module is one such useful module, which has pulled in the interest of numerous specialists in this emerging area. Recent developments are focused on the development of IoT based smart irrigation modules for Controlled Environment Agriculture (CEA). An affordable and simple type of system module is developed by using Arduinobased modules for the irrigation controller system framework. These Arduino-based irrigation modules are helpful to manage different ecological factors like dampness, temperature, and measure of water needed by the harvests. Different sensors like water stream sensors and soil dampness sensors are used as part of the system module prototype development. Reports are gathered and analyzed by the Arduino-based controller for the standard estimations of various factors needed by harvest. In this paper, a NodeMCU based smart irrigation module is developed using sensors like Soil moisture sensor, Temperature sensor, and ESP8266 WiFi Module and tested.
