Precision Agriculture 

About: Precision Agriculture is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Precision agriculture & Spatial variability. It has an ISSN identifier of 1385-2256. Over the lifetime, 1748 publications have been published receiving 44748 citations.

The application of small unmanned aerial systems for precision agriculture: a review

Abstract: Precision agriculture (PA) is the application of geospatial techniques and sensors (e.g., geographic information systems, remote sensing, GPS) to identify variations in the field and to deal with them using alternative strategies. In particular, high-resolution satellite imagery is now more commonly used to study these variations for crop and soil conditions. However, the availability and the often prohibitive costs of such imagery would suggest an alternative product for this particular application in PA. Specifically, images taken by low altitude remote sensing platforms, or small unmanned aerial systems (UAS), are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. Not surprisingly, there have been several recent studies in the application of UAS imagery for PA. The results of these studies would indicate that, to provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required. Moreover, it is suggested that such endeavors should involve the farmer, particularly in the process of field design, image acquisition, image interpretation and analysis.

Future Directions of Precision Agriculture

Abstract: Precision Agriculture is advancing but not as fast as predicted 5 years ago. The development of proper decision-support systems for implementing precision decisions remains a major stumbling block to adoption. Other critical research issues are discussed, namely, insufficient recognition of temporal variation, lack of whole-farm focus, crop quality assessment methods, product tracking and environmental auditing. A generic research programme for precision agriculture is presented. A typology of agriculture countries is introduced and the potential of each type for precision agriculture discussed.

Precision Agriculture and Sustainability

Abstract: Precision Agriculture (PA) can help in managing crop production inputs in an environmentally friendly way. By using site-specific knowledge, PA can target rates of fertilizer, seed and chemicals for soil and other conditions. PA substitutes information and knowledge for physical inputs. A literature review indicates PA can contribute in many ways to long-term sustainability of production agriculture, confirming the intuitive idea that PA should reduce environmental loading by applying fertilizers and pesticides only where they are needed, and when they are needed. Precision agriculture benefits to the environment come from more targeted use of inputs that reduce losses from excess applications and from reduction of losses due to nutrient imbalances, weed escapes, insect damage, etc. Other benefits include a reduction in pesticide resistance development. One limitation of the papers reviewed is that only a few actually measured directly environmental indices, such as leaching with the use of soil sensors. Most of them estimated indirectly the environmental benefits by measuring the reduced chemical loading. Results from an on-farm trial in Argentina provide an example of how site-specific information and variable rate application could be used in maintaining profitability while reducing N applications. Results of the sensitivity analysis show that PA is a modestly more profitable alternative than whole field management, for a wide range of restrictions on N application levels. These restrictions might be government regulations or the landowner's understanding of environmental stewardship. In the example, variable rate of N maintains farm profitability even when nitrogen is restricted to less than half of the recommended uniform rate.

Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status

Abstract: Remote sensing is a key technology for precision agriculture to assess actual crop conditions. Commercial, high-spatial-resolution imagery from aircraft and satellites are expensive so the costs may outweigh the benefits of the information. Hobbyists have been acquiring aerial photography from radio-controlled model aircraft; we evaluated these very-low-cost, very high-resolution digital photography for use in estimating nutrient status of corn and crop biomass of corn, alfalfa, and soybeans. Based on conclusions from previous work, we optimized an aerobatic model aircraft for acquiring pictures using a consumer-oriented digital camera. Colored tarpaulins were used to calibrate the images; there were large differences in digital number (DN) for the same reflectance because of differences in the exposure settings selected by the digital camera. To account for differences in exposure a Normalized Green–Red Difference Index [(NGRDI = (Green DN − Red DN)/(Green DN + Red DN)] was used; this index was linearly related to the normalized difference of the green and red reflectances, respectively. For soybeans, alfalfa and corn, dry biomass from zero to 120 g m−2 was linearly correlated to NGRDI, but for biomass greater than 150 g m−2 in corn and soybean, NGRDI did not increase further. In a fertilization experiment with corn, NGRDI did not show differences in nitrogen status, even though areas of low nitrogen status were clearly visible on late-season digital photographs. Simulations from the SAIL (Scattering of Arbitrarily Inclined Leaves) canopy radiative transfer model verified that NGRDI would be sensitive to biomass before canopy closure and that variations in leaf chlorophyll concentration would not be detectable. There are many advantages of model aircraft platforms for precision agriculture; currently, the imagery is best visually interpreted. Automated analysis of within-field variability requires more work on sensors that can be used with model aircraft platforms.

Farm and operator characteristics affecting the awareness and adoption of precision agriculture technologies in the US

Abstract: Precision agriculture (PA) technologies have been commercially available since the early 1990s. However, not only has the pace of adoption in the US been relatively modest but a surprisingly large number of producers are not familiar with these technologies. Using farm level survey data, this study quantifies the role that awareness plays in the decision to adopt PA technology and allows us to explore the potential for public or private information programs to affect the diffusion of PA. PA adoption and awareness are modeled as jointly determined dichotomous variables and their determinants are estimated using a two-stage (i.e. instrumental variable) logistic specification. The first-stage logit model indicated that operator education and computer literacy, full-time farming, and farm size positively affected the probability of PA awareness while the effect of age was negative. Grain and oilseed farms (i.e. corn, soybean, and small grains) and specialty crop farms (i.e. fruits, vegetables, and nuts) as well as farms located in the Heartland and Northern Great Plains regions were most likely to be aware of PA technologies. The second-stage PA adoption logit model, which included an instrumental variable to account for the endogeneity of awareness, revealed that farm size, full-time farming, and computer literacy positively influenced the likelihood of PA adoption. Grain and oilseed farms were the most likely types of farms to adopt PA as were farms in the Heartland region. Awareness, as defined in this study, was not found to be limiting the adoption of PA, suggesting that farmers for whom the technology is profitable are already aware of the technology and that a sector-wide public or private initiative to disseminate PA information would not likely have a major impact on PA diffusion.