Edge Computing Driven Data Sensing Strategy in the Entire Crop Lifecycle for Smart Agriculture

In the context of smart agriculture, high-value data sensing in the entire crop lifecycle is fundamental for realizing crop cultivation control. However, the existing data sensing methods are deficient regarding the sensing data value, poor data correlation, and high data collection cost. The main problem for data sensing over the entire crop lifecycle is how to sense high-value data according to crop growth stage at a low cost. To solve this problem, a data sensing framework was developed by combining edge computing with the Internet of Things, and a novel data sensing strategy for the entire crop lifecycle is proposed in this paper. The proposed strategy includes four phases. In the first phase, the crop growth stage is divided by Gath-Geva (GG) fuzzy clustering, and the key growth parameters corresponding to the growth stage are extracted. In the second phase, based on the current crop growth information, a prediction method of the current crop growth stage is constructed by using a Tkagi-Sugneo (T-S) fuzzy neural network. In the third phase, based on Deng’s grey relational analysis method, the environmental sensing parameters of the corresponding crop growth stage are optimized. In the fourth phase, an adaptive sensing method of sensing nodes with effective sensing area constraints is established. Finally, based on the actual crop growth history data, the whole crop life cycle dataset is established to test the performance and prediction accuracy of the proposed method for crop growth stage division. Based on the historical data, the simulation data sensing environment is established. Then, the proposed algorithm is tested and compared with the traditional algorithms. The comparison results show that the proposed strategy can divide and predict a crop growth cycle with high accuracy. The proposed strategy can significantly reduce the sensing and data collection times and energy consumption and significantly improve the value of sensing data.

Leaching Estimation for Paddy Crop using IoT and Machine Learning

Soil salinity is a major issue in farming faced by many farmers across the globe. So it is very much important to identify the salinity level of the soil. Internet of Things (IoT) assisted solution is proposed to determine Electric Conductivity, temperature, and Moisture level at the root zone of the crop field. Internet of Things (IoT) and Machine Learning (ML), based leaching water requirements estimation for saline soils is made using the onsite monitoring of the salinity level and crop field temperature and crop growth stage. Food and Agricultural Organization (FAO) proposed method of leaching requirement is implemented for efficient leaching water estimation. These parameters are used to train and test the Machine learning model to predict the leaching requirement. The performance of machine learning is measured in terms of accuracy.

Is what you see what you get? The relationship between field observed and actual aphid parasitism rates in canola crops

BACKGROUNDEstimating parasitoid abundance in the field can be difficult, even more so when attempting to quantify parasitism rates and the ecosystem service of biological control that parasitoids can provide. To understand how ‘observed’ parasitism rates (in-field mummy counts) of the green peach aphid, Myzus persicae (Sulzer) (Homoptera: Aphididae) translate to ‘actual’ parasitism rates (laboratory-reared parasitoid counts), field work was undertaken in Australian canola fields over a growing season. Parasitoids were reared within a controlled laboratory setting.RESULTSTotal observed and actual parasitism rates of M. persicae varied considerably across regions, but less so on a field level. Overall, actual parasitism was on average 2.4 times higher than that observed in the field, with rates an average of 4-fold higher in South Australia. As crop growth stage progressed, the percentage of mummies observed increased. Percentage of parasitoids reared also increased with crop growth stage, averaging 3.4% during flowering and reaching 14.4% during podding/senescing. Although there was a greater diversity of reared parasitoid species at later crop growth stages, actual parasitism rate was unaffected by parasitoid species. Diaeretiella rapae was the most commonly reared parasitoid, increasing in abundance with crop growth stage.CONCLUSIONThese findings indicate that mummy counts alone do not provide a clear representation of parasitism within fields.

Warming temperatures lead to reduced summer carbon sequestration in the U.S. Corn Belt

The response of highly productive croplands at northern mid-latitudes to climate change is a primary source of uncertainty in the global carbon cycle, and a concern for future food production. We present a decadal time series (2007 to 2019) of hourly CO2 concentration measured at a very tall tower in the United States Corn Belt. Analyses of this record, with other long-term data in the region, reveal that warming has had a positive impact on net CO2 uptake during the early crop growth stage, but has reduced net CO2 uptake in both croplands and natural ecosystems during the peak growing season. Future increase in summer temperature is projected to reduce annual CO2 sequestration in the Corn Belt by 10–20%. These findings highlight the dynamic control of warming on cropland CO2 exchange and crop yields and challenge the paradigm that warming will continue to favor CO2 sequestration in northern mid-latitude ecosystems.

Fungi Associated with Garlic During the Cropping Season, with Focus on Fusarium proliferatum and F. oxysporum

Fusarium proliferatum has been reported as the main causal agent of garlic dry rot during the postharvest stage, but information on this fungus during the crop growth stage is lacking. We focused on the cropping season of garlic (Allium sativum L.) in the field, until its harvest, with the aim of clarifying the role of F. proliferatum in bulb infection as well as the impact of crop growing conditions on pathogen-plant interaction. Studies were conducted in Piacenza (northern Italy) for three seasons from 2016 to 2019. Six garlic farms were sampled. A different field was sampled every year. Soil samples were recovered at sowing time for the counting of fungal colony forming units (CFU). Plant samples were collected at three growth stages, from BBCH 15 (fifth leaf visible) to BBCH 49 (ripening), for which disease severity assessment and fungi isolations were performed. Fusarium was the most frequently isolated genus, of which F. proliferatum and F. oxysporum were the dominant species. F. proliferatum registered the highest incidence in all the farms tested, but F. oxysporum was dominant in the first year of the study. F. oxysporum incidence was correlated with dry weather, whereas F. proliferatum was correlated with rainy weather. In conclusion, our result confirms the association of F. proliferatum with garlic bulbs from the crop’s early growth stages, suggesting potential seed transmission as a source of this fungal pathogen. Further studies should investigate the link between fusaria occurrence in the field and dry rot outbreaks occurring postharvest and during storage of garlic.

Hymenopteran Parasitoids of Aphid Pests within Australian Grain Production Landscapes

In grain crops, aphids are important pests, but they can be suppressed by hymenopteran parasitoids. A challenge in incorporating parasitoids into Integrated Pest Management (IPM) programs, however, is that parasitoid numbers can be low during periods within the season when aphids are most damaging. Understanding the population dynamics of key aphid species and their parasitoids is central to ameliorating this problem. To examine the composition and seasonal trends of both aphid and parasitoid populations in south-eastern Australia, samples were taken throughout the winter growing seasons of 2017 and 2018 in 28 fields of wheat and canola. Myzus persicae (Sulzer) was the most abundant aphid species, particularly within canola crops. Across all fields, aphid populations remained relatively low during the early stages of crop growth and increased as the season progressed. Seasonal patterns were consistent across sites, due to climate, crop growth stage, and interactions between these factors. For canola, field edges did not appear to act as reservoirs for either aphids or parasitoids, as there was little overlap in the community composition of either, but for wheat there was much similarity. This is likely due to the presence of similar host plants within field edges and the neighbouring crop, enabling the same aphid species to persist within both areas. Diaeretiella rapae (M’Intosh) was the most common parasitoid across our study, particularly in canola, yet was present only in low abundance at field edges. The most common parasitoid in wheat fields was Aphidius matricariae (Haliday), with field edges likely acting as a reservoir for this species. Secondary parasitoid numbers were consistently low across our study. Differences in parasitoid species composition are discussed in relation to crop type, inter-field variation, and aphid host. The results highlight potential focal management areas and parasitoids that could help control aphid pests within grain crops.

Soil Moisture based Automatic Irrigation System to Improve Water Productivity

Irrigation automation plays a vital role in agricultural water management system. An efficient automatic irrigation system is crucial to improve crop water productivity. Soil moisture based irrigation is an economical and efficient approach for automation of irrigation system. An experiment was conducted for irrigation automation based on the soil moisture content and crop growth stage. The experimental findings exhibited that, automatic irrigation system based on the proposed model triggers the water supply accurately based on the real-time soil moisture values.

Review on the Effect of Irrigation Interval on Different Crop Production

In this review, the effect of irrigation intervals on growth and yield of onion, maize yield, growth characteristics for Chile pepper, vegetative growth and yield, growth analysis of soybean, forage production, growth and development of tomato, the effect of irrigation level and irrigation frequency on the growth of mini Chinese cabbage and Influence of irrigation interval, nitrogen level and crop geometry on production lettuce have been reviewed. The best performance irrigation interval for onion, maize, pepper, okra, soybean, forage, tomato, cabbage and lettuce are 5, 6, 1, 12, 8, 20, 1, 4 and 2 day respectively. Crop type, crop growth stage soil type, climate condition (temperature, rainfall, humidity, sunshine hour and wend speed) duration of the environment should be properly addressed and potential evapotranspiration and reference evapotranspiration should be estimated for determining of irrigating interval. In these cases, some of the studies are properly addressed these important parameters but some of the study not indicates. On the other hand chemical composition of water and soil, fertilizer application, method of research design and plant geometry are should be identified to eradicate the misjudgment of your best productivity of irrigation interval.