Development of canopy vigour maps using UAV for site-specific management during vineyard spraying process

Site-specific management of crops represents an important improvement in terms of efficiency and efficacy of the different labours, and its implementation has experienced a large development in the last decades, especially for field crops. The particular case of the spray application process for what are called “specialty crops” (vineyard, orchard fruits, citrus, olive trees, etc.)FI-DGR grant from Generalitat de Catalunya (2018 FI_B1 00083). Research and improvement of Dosaviña have been developed under LIFE PERFECT project: Pesticide Reduction using Friendly and Environmentally Controlled Technologies (LIFE17 ENV/ES/000205)This research was partially funded by the “Ajuts a les activitats de demostració (operació 01.02.01 de Transferència Tecnològica del Programa de desenvolupament rural de Catalunya 2014-2020)” and an FI-DGR grant from Generalitat de Catalunya (2018 FI_B1 00083). Research and improvement of Dosaviña have been developed under the LIFE PERFECT project: Pesticide Reduction using Friendly and Environmentally Controlled Technologies (LIFE17 ENV/ES/000205).This research was partially funded by the “Ajuts a les activitats de demostració (operació 01.02.01 de Transferència Tecnològica del Programa de desenvolupament rural de Catalunya 2014-2020)” and an FI-DGR grant from Generalitat de Catalunya (2018 FI_B1 00083). Research and improvement of Dosaviña have been developed under LIFE PERFECT project: Pesticide Reduction using Friendly and Environmentally Controlled Technologies (LIFE17 ENV/ES/000205)Postprint (updated version

Selected bibliography of remote sensing

Bibliography of remote sensing techniques for analysis and assimilation of geographic dat

The Determinants of Technology Adoption by UK Farmers using Bayesian Model Averaging. The Cases of Organic Production and Computer Usage.

We introduce and implement a reversible jump approach to Bayesian Model Averaging for the Probit model with uncertain regressors. This approach provides a direct estimate of the probability that a variable should be included in the model. Two applications are investigated. The �rst is the adoption of organic systems in UK farming, and the second is the in�uence of farm and farmer characteristics on the use of a computer on the farm. While there is a correspondence between the conclusions we would obtain with and without model averaging results, we �find important di¤erences, particularly in smaller samples.Agriculture, Adoption, Model Averaging, Organic, Computer

Development and evaluation on a wireless multi-gas-sensors system for improving traceability and transparency of table grape cold chain

There is increasing requirement to improve traceability and transparency of table grapes cold chain. Key traceability indicators including temperature, humidity and gas microenvironments (e.g., CO2, O2, and SO2) based on table grape cold chain management need to be monitored and controlled. This paper presents a Wireless Multi-Gas-Sensors System (WGS2) as an effective real-time cold chain monitoring system, which consists of three units: (1) the WMN which applies the 433 MHz as the radio frequency to increase the transmission performance and forms a wireless sensor network; (2) the WAN which serves as the intermediary to connect the users and the sensor nodes to keep the sensor data without delay by the GPRS remote transmission module; (3) the signal processing unit which contains embedded software to drive the hardware to normal operation and shelf life prediction for table grapes. Then the study evaluates the WGS2 in a cold chain scenario and analyses the monitoring data. The results show that the WGS2 is effective in monitoring quality, and improving transparency and traceability of table grape cold chains. Its deploy ability and efficiency in implantation can enable the establishment of a more efficient, transparent and traceable table grape supply chain.N/

The role of RFID in agriculture: Applications, limitations and challenges

The recent advances in RFID offer vast opportunities for research, development and innovation in agriculture. The aim of this paper is to give readers a comprehensive view of current applications and new possibilities, but also explain the limitations and challenges of this technology. RFID has been used for years in animal identification and tracking, being a common practice in many farms. Also it has been used in the food chain for traceability control. The implementation of sensors in tags, make possible to monitor the cold chain of perishable food products and the development of new applications in fields like environmental monitoring, irrigation, specialty crops and farm machinery. However, it is not all advantages. There are also challenges and limitations that should be faced in the next years. The operation in harsh environments, with dirt, extreme temperatures; the huge volume of data that are difficult to manage; the need of longer reading ranges, due to the reduction of signal strength due to propagation in crop canopy; the behavior of the different frequencies, understanding what is the right one for each application; the diversity of the standards and the level of granularity are some of them

Mismeasurement of the CPI

In this paper, we investigate several key problems in Japanese economic statistics. We use CPI mismeasurements and biases as an example to explore the roots of the problems and also to offer guidelines for improvements. We emphasize 3 major shortcomings shared by many official statistics in Japan: (1) long delays in adjustments, (2) lack of proper coordination, and 3) insufficient information disclosure. In the analysis of CPI bias, we limit our focus to potential biases due to aggregation, survey methodology and sample selection procedures. We estimate that, in recent years, the commodity CPI inflation rate is biased upward by at least 0.5% per year, even if we assume away the potential bias associated with the quality adjustment, delay in incorporating changes in consumption basket, and other important unresolved problems.

A Review on Tomato Leaf Disease Detection using Deep Learning Approaches

Agriculture is one of the major sectors that influence the India economy due to the huge population and ever-growing food demand. Identification of diseases that affect the low yield in food crops plays a major role to improve the yield of a crop. India holds the world's second-largest share of tomato production. Unfortunately, tomato plants are vulnerable to various diseases due to factors such as climate change, heavy rainfall, soil conditions, pesticides, and animals. A significant number of studies have examined the potential of deep learning techniques to combat the leaf disease in tomatoes in the last decade. However, despite the range of applications, several gaps within tomato leaf disease detection are yet to be addressed to support the tomato leaf disease diagnosis. Thus, there is a need to create an information base of existing approaches and identify the challenges and opportunities to help advance the development of tools that address the needs of tomato farmers. The review is focussed on providing a detailed assessment and considerations for developing deep learning-based Convolutional Neural Networks (CNNs) architectures like Dense Net, ResNet, VGG Net, Google Net, Alex Net, and LeNet that are applied to detect the disease in tomato leaves to identify 10 classes of diseases affecting tomato plant leaves, with distinct trained disease datasets. The performance of architecture studies using the data from plantvillage dataset, which includes healthy and diseased classes, with the assistance of several different architectural designs. This paper helps to address the existing research gaps by guiding further development and application of tools to support tomato leaves disease diagnosis and provide disease management support to farmers in improving the crop