Enhancing Farm-Level Decision Making through Innovation

New information and knowledge are important aspects of innovation in modern farming systems. There is currently an abundance of digital and data-driven solutions that can potentially transform our food systems. At a time when the general public has concerns about how food is produced and the impact of farm production systems on the environment, strategies to increase public acceptance and the sustainability of food production are required more than ever. New tools and technology can provide timely insights into aspects such as nutrient profiles, the tracking of animal or plant wellbeing, and land-use options to enhance inputs and outputs associated with the farm business. Such solutions have the ultimate aim of enhancing production efficiency and contributing to the process of learning about the advantages of the innovation, while ensuring more sustainable food supplies. At the farm level, any new information needs to be in a useful format and beneficial for management and farm decision-making. The papers in this Special Issue evaluate agri-business innovation that can enhance farm-level decision-making

Just-in-time Pastureland Trait Estimation for Silage Optimization, under Limited Data Constraints

To ensure that pasture-based farming meets production and environmental targets for a growing population under increasing resource constraints, producers need to know pastureland traits. Current proximal pastureland trait prediction methods largely rely on vegetation indices to determine biomass and moisture content. The development of new techniques relies on the challenging task of collecting labelled pastureland data, leading to small datasets. Classical computer vision has already been applied to weed identification and recognition of fruit blemishes using morphological features, but machine learning algorithms can parameterise models without the provision of explicit features, and deep learning can extract even more abstract knowledge although typically this is assumed to be based around very large datasets. This work hypothesises that through the advantages of state-of-the-art deep learning systems, pastureland crop traits can be accurately assessed in a just-in-time fashion, based on data retrieved from an inexpensive sensor platform, under the constraint of limited amounts of labelled data. However the challenges to achieve this overall goal are great, and for applications such as just-in-time yield and moisture estimation for farm-machinery, this work must bring together systems development, knowledge of good pastureland practice, and also techniques for handling low-volume datasets in a machine learning context. Given these challenges, this thesis makes a number of contributions. The first of these is a comprehensive literature review, relating pastureland traits to ruminant nutrient requirements and exploring trait estimation methods, from contact to remote sensing methods, including details of vegetation indices and the sensors and techniques required to use them. The second major contribution is a high-level specification of a platform for collecting and labelling pastureland data. This includes the collection of four-channel Blue, Green, Red and NIR (VISNIR) images, narrowband data, height and temperature differential, using inexpensive proximal sensors and provides a basis for holistic data analysis. Physical data platforms built around this specification were created to collect and label pastureland data, involving computer scientists, agricultural, mechanical and electronic engineers, and biologists from academia and industry, working with farmers. Using the developed platform and a set of protocols for data collection, a further contribution of this work was the collection of a multi-sensor multimodal dataset for pastureland properties. This was made up of four-channel image data, height data, thermal data, Global Positioning System (GPS) and hyperspectral data, and is available and labelled with biomass (Kg/Ha) and percentage dry matter, ready for use in deep learning. However, the most notable contribution of this work was a systematic investigation of various machine learning methods applied to the collected data in order to maximise model performance under the constraints indicated above. The initial set of models focused on collected hyperspectral datasets. However, due to their relative complexity in real-time deployment, the focus was instead on models that could best leverage image data. The main body of these models centred on image processing methods and, in particular, the use of the so-called Inception Resnet and MobileNet models to predict fresh biomass and percentage dry matter, enhancing performance using data fusion, transfer learning and multi-task learning. Images were subdivided to augment the dataset, using two different patch sizes, resulting in around 10,000 small patches of size 156 x 156 pixels and around 5,000 large patches of size 240 x 240 pixels. Five-fold cross validation was used in all analysis. Prediction accuracy was compared to older mechanisms, albeit using hyperspectral data collected, with no provision made for lighting, humidity or temperature. Hyperspectral labelled data did not produce accurate results when used to calculate Normalized Difference Vegetation Index (NDVI), or to train a neural network (NN), a 1D Convolutional Neural Network (CNN) or Long Short Term Memory (LSTM) models. Potential reasons for this are discussed, including issues around the use of highly sensitive devices in uncontrolled environments. The most accurate prediction came from a multi-modal hybrid model that concatenated output from an Inception ResNet based model, run on RGB data with ImageNet pre-trained RGB weights, output from a residual network trained on NIR data, and LiDAR height data, before fully connected layers, using the small patch dataset with a minimum validation MAPE of 28.23% for fresh biomass and 11.43% for dryness. However, a very similar prediction accuracy resulted from a model that omitted NIR data, thus requiring fewer sensors and training resources, making it more sustainable. Although NIR and temperature differential data were collected and used for analysis, neither improved prediction accuracy, with the Inception ResNet model’s minimum validation MAPE rising to 39.42% when NIR data was added. When both NIR data and temperature differential were added to a multi-task learning Inception ResNet model, it yielded a minimum validation MAPE of 33.32%. As more labelled data are collected, the models can be further trained, enabling sensors on mowers to collect data and give timely trait information to farmers. This technology is also transferable to other crops. Overall, this work should provide a valuable contribution to the smart agriculture research space

Review:New sensors and data-driven approaches—A path to next generation phenomics

At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for “next generation phenomics” based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts

A review of precision technologies in pasture-based dairying systems

peer-reviewedGrassland-based dairy production provides multiple benefits to farmers and to the wider society, but the European grassland area has been significantly reduced during the last decades. This paper aims to explore societal and economic options to support grassland-based dairy production in Europe. In the recent past, several societal initiatives have emerged to stimulate grassland-based dairy production: treaties, premiums and market concepts. When developing stimulating initiatives, the mindset of the farmer should be taken into account. Farmers are key actors when it comes to maintaining and improving grassland-based dairy production systems since they decide on the day-to-day management of the farm. To maintain grassland-based dairy production and to preserve the associated ecosystem services, it is, therefore, necessary to clearly show the importance of this production system for society to the farmers (show the customer perspective) and to support this by valuing the products from these systems accordingly. “New” business models should financially reward farmers for their added value contributions in delivering ecosystem services

Development and Examination of Mobile Sensor Systems and Software Applications for Use in Estimation of Forage Dry Matter Biomass and Crude Protein

The use of field-based sensors can generate large amounts of data rapidly for phenomic modeling and management decisions; however some challenges may be encountered. AgriLogger software developed to rapidly acquire data for predictive model construction and implementation. AgriLogger features include user controls for data acquisition rate and a single output file for multiple sensors. Temporal and spatial data parsing was achieved from position and time stamps. Non-destructive biomass estimation of vegetation has been performed via remote sensing. This study examined several types of ground-based mobile sensing strategies for forage biomass estimation in alfalfa (Medicago sativa L.), bermudagrass [Cynodon dactylon (L.) Pers.], and wheat (Triticum aestivum L.). Forage quality analysis has historically been performed on physically collected samples through laboratory methods. Developing a sensor system which can collect data and provide estimates for crude protein (CP) in a more timely manner will allow near real time decision making by mangers. To evaluate the feasibility of such a system bermudagrass tall fescue (Festuca arundinacea Schreb.), and wheat were examined. AgriLogger reduced the post-processing time by a factor of 10 and data acquisition time by a factor of 60 as compared to commercially available alternatives which could be used for sensor data acquisition on vegetation. Predictive models were constructed via partial least squares regression and modeled estimates were compared to the physically measured biomass and CP. Differences between methods were minimal (average percent error of 11.2% for difference between predicted values versus machine and quadrat harvested biomass values (1.64 and 4.91 t ha-1, respectively). The predicted CP regressed with those measured in a laboratory using NIRS produced an R2 of 0.75 for a hyperspectral model. Wheat model prediction of crude protein bore n R2 of 0.65 and tall fescue R2=0.83. These data suggest that using mobile sensor-based biomass and CP estimation models could be an effective alternative to the traditional clipping and laboratory methods for rapid, accurate in-field estimation.Plant & Soil Science

Unveiling the potential of proximal hyperspectral sensing for measuring herbage nutritive value in a pasture-based dairy farm system : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agriculture and Horticulture at Massey University, Manawatū, New Zealand

The aim of this thesis was to unveil the potential of proximal hyperspectral sensing for measuring herbage nutritive value in a pasture based-dairy farm system. Hyperspectral canopy reflectance and herbage cuts as well as data on herbage and supplement allocation, and milk production were collected regularly from Dairy 1 farm at Massey University during the 2016-17 and 2017-18 production seasons. Milk, fat and protein yields and body condition score of cows were measured at monthly herd tests while live weights were recorded daily. Calibration equations determining herbage the nutritive value traits digestible organic matter in dry matter, metabolisable energy (ME), crude protein, neutral detergent fibre and acid detergent fibre from hyperspectral canopy reflectance data were developed and validated using partial least squares regression. Canopy reflectance calibration models were able to determine the various herbage nutritive value traits with R2 values ranging from 0.57 to 0.78. Variation of herbage nutritive value traits were mostly explained by month within production season (42.7% of variance among traits) followed by random error (33.4%), production season (13.1%) and paddock (10.7%). The relative importance of herbage nutritive value and other herbage quantity and climate-related variables in driving performance per cow in the herd was determined using multiple linear regression. Herbage metabolizable energy explained 20% to 30% of milk, fat and protein production per cow while herbage quantity and climate- related factors were relatively less important (below 15%). Random regression models were used to model lactation curves of milk, fat, protein and live weight to estimate daily ME requirements of individual cows. The daily ME estimated requirements was nearly a fifth above or below the daily mean ME supplied. The deviation of the daily ME estimated requirements of a cow from the actual ME supplied per cow in the herd was mostly explained by the observations made within a cow rather than between cows or breeds. Variation in herbage nutritive value in addition to the within and between cow variation of ME estimated requirements were high enough to justify the use of proximal hyperspectral sensing as measurement tool to assist with feed allocation decision-making. However, the potential of this technology could be further enhanced using more precise technologies to allocate herbage to individual cows or groups of cows. The potential benefits of more precise feed allocation will result in more efficient grazing management and thus improved utilisation of herbage and hence milk production

Earth Resources. A continuing bibliography with indexes, issue 34, July 1982

This bibliography lists 567 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System between April 1, and June 30, 1982. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis