595 research outputs found
New strategies for row-crop management based on cost-effective remote sensors
Agricultural technology can be an excellent antidote to resource scarcity. Its growth has
led to the extensive study of spatial and temporal in-field variability. The challenge of
accurate management has been addressed in recent years through the use of accurate
high-cost measurement instruments by researchers. However, low rates of technological
adoption by farmers motivate the development of alternative technologies based on
affordable sensors, in order to improve the sustainability of agricultural biosystems.
This doctoral thesis has as main objective the development and evaluation of systems
based on affordable sensors, in order to address two of the main aspects affecting the
producers: the need of an accurate plant water status characterization to perform a
proper irrigation management and the precise weed control.
To address the first objective, two data acquisition methodologies based on aerial
platforms have been developed, seeking to compare the use of infrared thermometry
and thermal imaging to determine the water status of two most relevant row-crops in the
region, sugar beet and super high-density olive orchards. From the data obtained, the
use of an airborne low-cost infrared sensor to determine the canopy temperature has
been validated. Also the reliability of sugar beet canopy temperature as an indicator its
of water status has been confirmed. The empirical development of the Crop Water Stress
Index (CWSI) has also been carried out from aerial thermal imaging combined with
infrared temperature sensors and ground measurements of factors such as water
potential or stomatal conductance, validating its usefulness as an indicator of water
status in super high-density olive orchards.
To contribute to the development of precise weed control systems, a system for detecting
tomato plants and measuring the space between them has been developed, aiming to
perform intra-row treatments in a localized and precise way. To this end, low cost optical
sensors have been used and compared with a commercial LiDAR laser scanner. Correct
detection results close to 95% show that the implementation of these sensors can lead
to promising advances in the automation of weed control.
The micro-level field data collected from the evaluated affordable sensors can help
farmers to target operations precisely before plant stress sets in or weeds infestation
occurs, paving the path to increase the adoption of Precision Agriculture techniques
Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review
Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks
The estimation of crop emergence in potatoes by UAV RGB imagery
Abstract Background Crop emergence and canopy cover are important physiological traits for potato (Solanum tuberosum L.) cultivar evaluation and nutrients management. They play important roles in variety screening, field management and yield prediction. Traditional manual assessment of these traits is not only laborious but often subjective. Results In this study, semi-automated image analysis software was developed to estimate crop emergence from high-resolution RGB ortho-images captured from an unmanned aerial vehicle (UAV). Potato plant objects were extracted from bare soil using Excess Green Index and Otsu thresholding methods. Six morphological features were calculated from the images to be variables of a Random Forest classifier for estimating the number of potato plants at emergence stage. The outputs were then used to estimate crop emergence in three field experiments that were designed to investigate the effects of cultivars, levels of potassium (K) fertiliser input, and new compound fertilisers on potato growth. The results indicated that RGB UAV image analysis can accurately estimate potato crop emergence rate in comparison to manual assessment, with correlation coefficient ( r2 ) of 0.96 and provide an efficient tool to evaluate emergence uniformity. Conclusions The proposed UAV image analysis method is a promising tool for use as a high throughput phenotyping method for assessing potato crop development at emergence stage. It can also facilitate future studies on optimizing fertiliser management and improving emergence consistency
Novel Methods for RGB Aerial Image Analysis
Multiple linear regression models were developed to predict sand and clay content along with soil organic matter content from RGB imagery from both commercially available satellite imagery as well as RGB UAV imagery. UAV Imagery was tested at two flight altitudes to determine if lower or higher altitude had an effect on prediction. In cases of sand, clay, and OM content, flight altitudes did not significantly differ in prediction abilities. Satellite imagery was evaluated using data from Planet Labs as well as Google Earth. Regression models were developed to predict sand, clay, and soil organic matter content from these satellite images, which captured fields with bare soil. An alternative to whole field data collection, referred to herein as the point sampling method, was introduced. A survey of currently available neural network and machine learning technologies was performed to establish which of these technologies could benefit the precision agriculture industry. A sample model was trained to detect and classify cotton blooms from low-altitude RGB imagery collected from a DJI Phantom 3 UAV
Semantic Segmentation based deep learning approaches for weed detection
Global increase in herbicide use to control weeds has led to issues such as evolution of herbicide-resistant weeds, off-target herbicide movement, etc. Precision agriculture advocates Site Specific Weed Management (SSWM) application to achieve precise and right amount of herbicide spray and reduce off-target herbicide movement. Recent advancements in Deep Learning (DL) have opened possibilities for adaptive and accurate weed recognitions for field based SSWM applications with traditional and emerging spraying equipment; however, challenges exist in identifying the DL model structure and train the model appropriately for accurate and rapid model applications over varying crop/weed growth stages and environment. In our study, an encoder-decoder based DL architecture was proposed that performs pixel-wise Semantic Segmentation (SS) classifications of crop, soil, and weed patches in the fields. The objective of this study was to develop a robust weed detection algorithm using DL techniques that can accurately and reliably locate weed infestations in low altitude Unmanned Aerial Vehicle (UAV) imagery with acceptable application speed. Two different encoder-decoder based SS models of LinkNet and UNet were developed using transfer learning techniques. We performed various measures such as backpropagation optimization and refining of the dataset used for training to address the class-imbalance problem which is a common issue in developing weed detection models. It was found that LinkNet model with ResNet18 as the encoder section and use of ‘Focal loss’ loss function was able to achieve the highest mean and class-wise Intersection over Union scores for different class categories while performing predictions on unseen dataset. The developed state-of-art model did not require a large amount of data during training and the techniques used to develop the model in our study provides a propitious opportunity that performs better than the existing SS based weed detections models. The proposed model integrates a futuristic approach to develop a model that could be used for weed detection on aerial imagery from UAV and perform real-time SSWM applications
Advisor: Yeyin Sh
Sustainable Approach to Weed Management: The Role of Precision Weed Management
In the last few decades, the increase in the world’s population has created a need to produce more food, generating, consequently, greater pressure on agricultural production. In addition, problems related to climate change, water scarcity or decreasing amounts of arable land have serious implications for farming sustainability. Weeds can affect food production in agricultural systems, decreasing the product quality and productivity due to the competition for natural resources. On the other hand, weeds can also be considered to be valuable indicators of biodiversity because of their role in providing ecosystem services. In this sense, there is a need to carry out an effective and sustainable weed management process, integrating the various control methods (i.e., cultural, mechanical and chemical) in a harmonious way, without harming the entire agrarian ecosystem. Thus, intensive mechanization and herbicide use should be avoided. Herbicide resistance in some weed biotypes is a major concern today and must be tackled. On the other hand, the recent development of weed control technologies can promote higher levels of food production, lower the amount of inputs needed and reduce environmental damage, invariably bringing us closer to more sustainable agricultural systems. In this paper, we review the most common conventional and non-conventional weed control strategies from a sustainability perspective, highlighting the application of the precision and automated weed control technologies associated with precision weed management (PWM).info:eu-repo/semantics/publishedVersio
Sustainable Approach to Weed Management: The Role of Precision Weed Management
In the last few decades, the increase in the world’s population has created a need to produce more food, generating, consequently, greater pressure on agricultural production. In addition, problems related to climate change, water scarcity or decreasing amounts of arable land have serious implications for farming sustainability. Weeds can affect food production in agricultural systems, decreasing the product quality and productivity due to the competition for natural resources. On the other hand, weeds can also be considered to be valuable indicators of biodiversity because of their role in providing ecosystem services. In this sense, there is a need to carry out an effective and sustainable weed management process, integrating the various control methods (i.e., cultural, mechanical and chemical) in a harmonious way, without harming the entire agrarian ecosystem. Thus, intensive mechanization and herbicide use should be avoided. Herbicide resistance in some weed biotypes is a major concern today and must be tackled. On the other hand, the recent development of weed control technologies can promote higher levels of food production, lower the amount of inputs needed and reduce environmental damage, invariably bringing us closer to more sustainable agricultural systems. In this paper, we review the most common conventional and non-conventional weed control strategies from a sustainability perspective, highlighting the application of the precision and automated weed control technologies associated with precision weed management (PWM).info:eu-repo/semantics/publishedVersio
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