Aims Grasslands are the world’s most extensive terrestrial ecosystem, and are a major feed source for livestock. Meeting increasing demand for meat and other dairy products in a sustainable manner is a big challenge. At a field scale, Global Positioning System and ground-based sensor technologies provide promising tools for grassland and herd management with high precision. With the growth in availability of spaceborne remote sensing data, it is therefore important to revisit the relevant methods and applications that can exploit this imagery. In this article, we have reviewed the (i) current status of grassland monitoring/observation methods and applications based on satellite remote sensing data, (ii) the technological and methodological developments to retrieve different grassland biophysical parameters and management characteristics (i.e. degradation, grazing intensity) and (iii) identified the key remaining challenges and some new upcoming trends for future development.

Important Findings The retrieval of grassland biophysical parameters have evolved in recent years from classical regression analysis to more complex, efficient and robust modeling approaches, driven by satellite data, and are likely to continue to be the most robust method for deriving grassland information, however these require more high quality calibration and validation data. We found that the hypertemporal satellite data are widely used for time series generation, and particularly to overcome cloud contamination issues, but the current low spatial resolution of these instruments precludes their use for field-scale application in many countries. This trend may change with the current rise in launch of satellite constellations, such as RapidEye, Sentinel-2 and even the microsatellites such as those operated by Skybox Imaging. Microwave imagery has not been widely used for grassland applications, and a better understanding of the backscatter behaviour from different phenological stages is needed for more reliable products in cloudy regions. The development of hyperspectral satellite instrumentation and analytical methods will help for more detailed discrimination of habitat types, and the development of tools for greater end-user operation.

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Satellite remote sensing of grasslands: from observation to management

The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The sector faces numerous challenges in order to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology. The main concept of AI in agriculture is its flexibility, high performance, accuracy, and cost-effectiveness. This paper presents a review of the applications of AI in soil management, crop management, weed management and disease management. A special focus is laid on the strength and limitations of the application and the way in utilizing expert systems for higher productivity.

/pdf/applications-of-artificial-intelligence-in-agriculture-a-4cx8o7onnh.pdf

Applications of Artificial Intelligence in Agriculture: A Review

Sustainable agricultural development is a significant solution with fast population development through the use of information and communication (ICT) in precision agriculture, which produced new methods for making cultivation further productive, proficient, well-regulated while preserving the climate. Big data (machine learning, deep learning, etc.) is amongst the vital technologies of ICT employed in precision agriculture for their huge data analytical capabilities to abstract significant information and to assist agricultural practitioners to comprehend well farming practices and take precise decisions. The main goal of this article is to acquire an awareness of the Big Data latest applications in smart agriculture and be acquainted with related social and financial challenges to be concentrated on. This article features data creation methods, accessibility of technology, accessibility of devices, software tools, and data analytic methods, and appropriate applications of big data in precision agriculture. Besides, there are still a few challenges that come across the widespread implementation of big data technology in agriculture.

/pdf/big-data-and-ai-revolution-in-precision-agriculture-survey-33nuz23dhn.pdf

Big Data and AI Revolution in Precision Agriculture: Survey and Challenges

Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

A novel approach for efficient crop yield prediction

Decision-making processes in agriculture often require reliable crop response models. The Fujian province of China is a mountainous region where weather aberrations such as typhoons, floods and droughts threaten rice production. Agricultural management specialists need simple and accurate estimation techniques to predict rice yields in the planning process. The objectives of the present study were to: (1) investigate whether artificial neural network (ANN) models could effectively predict Fujian rice yield for typical climatic conditions of the mountainous region, (2) evaluate ANN model performance relative to variations of developmental parameters and (3) compare the effectiveness of multiple linear regression models with ANN models. Models were developed using historical yield data at multiple locations throughout Fujian. Field-specific rainfall data and the weather variables (daily sunshine hours, daily solar radiation, daily temperature sum and daily wind speed) were used for each location. Adjusting ANN parameters such as learning rate and number of hidden nodes affected the accuracy of rice yield predictions. Optimal learning rates were between 0·71 and 0·90. Smaller data sets required fewer hidden nodes and lower learning rates in model optimization. ANN models consistently produced more accurate yield predictions than regression models. ANN rice grain yield models for Fujian resulted in R2 and RMSE of 0·87 and 891 vs 0·52 and 1977 for linear regression, respectively. Although more time consuming to develop than multiple linear regression models, ANN models proved to be superior for accurately predicting rice yields under typical Fujian climatic conditions.

Artificial neural networks for rice yield prediction in mountainous regions

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Artificial neural networks for rice yield prediction in mountainous regions