scholarly journals From Conventional to Precision Fertilization: A Case Study on the Transition for a Small-Medium Farm

2021 ◽  
Vol 3 (2) ◽  
pp. 438-446
Author(s):  
Massimo Brambilla ◽  
Elio Romano ◽  
Pietro Toscano ◽  
Maurizio Cutini ◽  
Marcello Biocca ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Crop Production ◽  
Production Quality ◽  
Soil Mapping ◽  
Variable Rate ◽  
Research Facility ◽  
Commercial Yield ◽  
Precision Fertilization ◽  
Production Dynamics

At the CREA research facility of Treviglio (Bergamo, Italy), to provide farmers with valuable hints for the transition from conventional to precision agriculture, information on crop production dynamics (Maize and Triticale) has been obtained using real-time soil mapping (resistivity technique) and production quality and quantity monitoring with a commercial yield mapping apparatus. The geostatistical processing of data resulted in the same zoning for Triticale, meaning that the characteristics of soil influenced crop behavior more than the variability resulting from other factors, which suggests that improvements in product yields can be planned and achieved acting, for instance, on variable rate distribution of fertilizers. The importance of the acquired data can help farmers to manage factors that are external to their plots of land.

scholarly journals Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Wei Zhao ◽  
William Yamada ◽  
Tianxin Li ◽  
Matthew Digman ◽  
Troy Runge
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Precision Agriculture ◽  
Crop Production ◽  
Domain Adaptation ◽  
Training Data ◽  
Detection Accuracy ◽  
Detection Model ◽  
Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

scholarly journals Challenges and limtations of site specific crop production applications of wheat and maize

2012 ◽  
pp. 101-104
Author(s):  
István Balla ◽  
Ákos Tarnawa ◽  
Csaba Horváth ◽  
Judit Kis ◽  
Márton Jolánkai
Keyword(s):  
Precision Agriculture ◽  
Crop Production ◽  
Production Management ◽  
Plant Density ◽  
Triticum Aestivum L ◽  
Site Specific ◽  
Small Plot ◽  
Management Techniques ◽  
The Impact

The development and implementation of precision agriculture or site-specific farming has been made possible by combining the Global Positioning System (GPS) and the Geographic Information Systems (GIS). Site specific agronomic applications are of high importance concerning the efficiency of management in crop production as well as the protection and maintenance of environment and nature. Precision crop production management techniques were applied at four locations to evaluate their impact on small plot units sown by wheat (Triticum aestivum L.) and maize (Zea mays L.) in a Hungarian national case study. The results obtained suggest the applicability of the site specific management techniques, however the crops studied responded in a different way concerning the impact of applications. Maize had a stronger response regarding grain yield and weed canopy. Wheat was responding better than maize concerning plant density and protein content performance.

scholarly journals DESIGN TRACEABILITY FOR INDONESIA AGRICULTURAL SUPPLY CHAIN BASED ON BLOCKCHAIN (CASE STUDY IN CHILLI COMMODITIES)

2021 ◽  
Vol 9 (09) ◽  
pp. 505-515
Author(s):  
Umi Marfuah ◽  
◽  
Yandra Arkeman ◽  
Machfud a ◽  
Indah Yuliasih ◽  
...  
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Crop Production ◽  
Production Quality ◽  
Multiple Stakeholders ◽  
Decentralized System ◽  
Agricultural Safety ◽  
High Degree ◽  
Agricultural Supply

Indonesians are the worlds largest chilli enthusiasts, mostly consuming fresh chilli. Because of chilli products generally perishable characteristics, its price has become unstable.The growing number of agricultural safety and risk issues has revealed a substantial need for an effective traceability solution, which serves as an essential agricultural supply chain method to ensure adequate product safety. Blockchain is the technology that disrupts goods in supply chains of agriculture and offers a revolutionary solution for their traceability. Today, farm supply chains are a dynamic ecosystem with multiple stakeholders, making it difficult to verify a range of main parameters, including the country of origin, stage in crop production, quality compliance, and yield monitoring. This paper suggests using the Ethereum blockchain and intelligent contracts to monitor and traceability operations across the agricultural supply chain effectively. Our proposed solutions remove the need for trustworthy centralized subjects, intermediaries, transaction records, performance, and security enhancements that are highly integral, accurate, and stable. The approach suggested focuses on using intelligent agreements to monitor and manage all communications and transactions between all actors in the supply chains ecosystem. All transactions are registered in the immutable blockchain lead with connections to a decentralized system (IPFS), ensuring the ecosystem is safe, confident, reliable and booming for everyones high degree of transparency and traceability.

scholarly journals Adoption of precision agriculture technologies in Ontario crop production

2018 ◽  
Vol 98 (6) ◽  
pp. 1384-1388 ◽  
Author(s):  
Sean Mitchell ◽  
Alfons Weersink ◽  
Bruce Erickson
Keyword(s):  
Precision Agriculture ◽  
Crop Production ◽  
Service Providers ◽  
Variable Rate ◽  
Global Positioning Systems ◽  
Agricultural Technologies ◽  
Positioning Systems ◽  
Vast Amount ◽  
Global Positioning

Ontario agricultural service providers were surveyed on their use of precision agricultural technologies. Global positioning systems are the most commonly adopted, while adoption rates for variable rate systems are significantly less. Enhancing adoption requires turning the vast amount of data collected on crop production into valuable decisions for the farmer.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

scholarly journals Economics of autonomous equipment for arable farms

2021 ◽  
Author(s):  
James Lowenberg-DeBoer ◽  
Kit Franklin ◽  
Karl Behrendt ◽  
Richard Godwin
Keyword(s):  
Precision Agriculture ◽  
Environmental Performance ◽  
Crop Production ◽  
Practical Experience ◽  
Production Costs ◽  
Farm Size ◽  
Medium Size ◽  
Economic Implications ◽  
The United Kingdom ◽  
Crop Agriculture

AbstractBy collecting more data at a higher resolution and by creating the capacity to implement detailed crop management, autonomous crop equipment has the potential to revolutionise precision agriculture (PA), but unless farmers find autonomous equipment profitable it is unlikely to be widely adopted. The objective of this study was to identify the potential economic implications of autonomous crop equipment for arable agriculture using a grain-oilseed farm in the United Kingdom as an example. The study is possible because the Hands Free Hectare (HFH) demonstration project at Harper Adams University has produced grain with autonomous equipment since 2017. That practical experience showed the technical feasibility of autonomous grain production and provides parameters for farm-level linear programming (LP) to estimate farm management opportunities when autonomous equipment is available. The study shows that arable crop production with autonomous equipment is technically and economically feasible, allowing medium size farms to approach minimum per unit production cost levels. The ability to achieve minimum production costs at relatively modest farm size means that the pressure to “get big or get out” will diminish. Costs of production that are internationally competitive will mean reduced need for government subsidies and greater independence for farmers. The ability of autonomous equipment to achieve minimum production costs even on small, irregularly shaped fields will improve environmental performance of crop agriculture by reducing pressure to remove hedges, fell infield trees and enlarge fields.

scholarly journals Predicting Shifts in Land Suitability for Maize Cultivation Worldwide Due to Climate Change: A Modeling Approach

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 295
Author(s):  
Yuan Gao ◽  
Anyu Zhang ◽  
Yaojie Yue ◽  
Jing’ai Wang ◽  
Peng Su
Keyword(s):  
Climate Change ◽  
Crop Production ◽  
Large Scale ◽  
Land Suitability ◽  
Large Scale Assessment ◽  
Scale Assessment ◽  
Maize Cultivation ◽  
Large Scale Screening ◽  
Selection Of

Suitable land is an important prerequisite for crop cultivation and, given the prospect of climate change, it is essential to assess such suitability to minimize crop production risks and to ensure food security. Although a variety of methods to assess the suitability are available, a comprehensive, objective, and large-scale screening of environmental variables that influence the results—and therefore their accuracy—of these methods has rarely been explored. An approach to the selection of such variables is proposed and the criteria established for large-scale assessment of land, based on big data, for its suitability to maize (Zea mays L.) cultivation as a case study. The predicted suitability matched the past distribution of maize with an overall accuracy of 79% and a Kappa coefficient of 0.72. The land suitability for maize is likely to decrease markedly at low latitudes and even at mid latitudes. The total area suitable for maize globally and in most major maize-producing countries will decrease, the decrease being particularly steep in those regions optimally suited for maize at present. Compared with earlier research, the method proposed in the present paper is simple yet objective, comprehensive, and reliable for large-scale assessment. The findings of the study highlight the necessity of adopting relevant strategies to cope with the adverse impacts of climate change.

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