Simulation and implementation of novel deep learning hardware architectures for resource constrained devices

Corey Lammie designed mixed signal memristive-complementary metal–oxide–semiconductor (CMOS) and field programmable gate arrays (FPGA) hardware architectures, which were used to reduce the power and resource requirements of Deep Learning (DL) systems; both during inference and training. Disruptive design methodologies, such as those explored in this thesis, can be used to facilitate the design of next-generation DL systems

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

A mixed-autonomous robotic platform for intra-row and inter-row weed removal for precision agriculture

The presence of weeds poses a common and persistent problem in crop cultivation, affecting both yield and overall agricultural productivity. Common solutions to the problem typically include chemical pesticides, mulching, or mechanical weeding performed by agricultural implements or humans. Even if effective, those techniques have several drawbacks, including soil and water pollution, high cost-effectiveness ratio or stress for operators. In recent years, novel robotic solutions have been proposed to overcome current limitations and to move towards more sustainable approaches to weeding. This work presents a mixed-autonomous, robotic, weeding system based on a fully integrated three-axis platform and a vision system mounted on a mobile rover. The rover’s motion is remotely controlled by a human operator, while weeds identification and removal is performed autonomously by the robotic system. Once in position, an RGB-D camera captures the portion of field to be treated. The acquired spatial, color and depth information is used to classify soil, the main crop, and the weeds to be removed using a pre-trained Deep Neural Network. Each target is then analyzed by a second RGB-D camera (mounted on the gripper) to confirm the correct classification before its removal. With the proposed approach, weeds are all the plants not classified as the main crop known a priori. The performance of the integrated robotic system has been tested in laboratory as well as in open field and in greenhouse conditions. The system was also tested under different light and shadowing conditions to evaluate the performance of the Deep Neural Network. Results show that the identification of the plants (both crop and weeds) is above 95%, increasing to 98% when additional information, such as the intra-row spacing, is provided. Nevertheless, the correct identification of the weeds remains above 97% ensuring an effective removal of weeds (up to 85%) with negligible crop damage (less than 5%)

A real-time phenotyping framework using machine learning for plant stress severity rating in soybean

Background: Phenotyping is a critical component of plant research. Accurate and precise trait collection, when integrated with genetic tools, can greatly accelerate the rate of genetic gain in crop improvement. However, efficient and automatic phenotyping of traits across large populations is a challenge; which is further exacerbated by the necessity of sampling multiple environments and growing replicated trials. A promising approach is to leverage current advances in imaging technology, data analytics and machine learning to enable automated and fast phenotyping and subsequent decision support. In this context, the workflow for phenotyping (image capture → data storage and curation → trait extraction → machine learning/classification → models/apps for decision support) has to be carefully designed and efficiently executed to minimize resource usage and maximize utility. We illustrate such an end-to-end phenotyping workflow for the case of plant stress severity phenotyping in soybean, with a specific focus on the rapid and automatic assessment of iron deficiency chlorosis (IDC) severity on thousands of field plots. We showcase this analytics framework by extracting IDC features from a set of ~4500 unique canopies representing a diverse germplasm base that have different levels of IDC, and subsequently training a variety of classification models to predict plant stress severity. The best classifier is then deployed as a smartphone app for rapid and real time severity rating in the field. Results: We investigated 10 different classification approaches, with the best classifier being a hierarchical classifier with a mean per-class accuracy of ~96%. We construct a phenotypically meaningful ‘population canopy graph’, connecting the automatically extracted canopy trait features with plant stress severity rating. We incorporated this image capture → image processing → classification workflow into a smartphone app that enables automated real-time evaluation of IDC scores using digital images of the canopy. Conclusion: We expect this high-throughput framework to help increase the rate of genetic gain by providing a robust extendable framework for other abiotic and biotic stresses. We further envision this workflow embedded onto a high throughput phenotyping ground vehicle and unmanned aerial system that will allow real-time, automated stress trait detection and quantification for plant research, breeding and stress scouting applications

2020 SDSU Data Science Symposium Program

https://openprairie.sdstate.edu/ds_symposium_programs/1002/thumbnail.jp

A Survey on Hybrid Techniques Using SVM

Support Vector Machines (SVM) with linear or nonlinear kernels has become one of the most promising learning algorithms for classification as well as for regression. All the multilayer perceptron (MLP),Radial Basic Function(RBF) and Learning Polynomials are also worked efficiently with SVM. SVM is basically derived from statistical Learning Theory and it is very powerful statistical tool. The basic principal for the SVM is structural risk minimization and closely related to regularization theory. SVM is a group of supervised learning techniques or methods, which is used to do for classification or regression. In this paper discussed the importance of Support Vector Machines in various areas. This paper discussing the efficiency of SVM with the combination of other classification techniques

Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy

A hatchery manual for the common, Chinese and Indian major carps (2nd rev ed.)

This major work on carp hatchery and nursery methods was part of an Asian Development Bank (ADB) project to improve carp seed production technology in ADB-member countries notably Bangladesh, Burma, Indonesia, Nepal, Pakistan and Sri Lanka. Designed as a reference source on carp seed production and as a mini-library for those stationed at seed production centers remote from scientific information channels.Fish culture, Hatcheries, Aquaculture techniques, Manuals Cyprinidae

Using Remote Sensing and Spatial Information Technologies to Detect and Map Two Aquatic Macrophytes

This paper describes the light reflectance characteristics ofwaterhyacinth [Eichhornia crassipes (Mort.) Solms] and hydrilla [Hydrilla verticillata (L.F.) Royle] and the application of airborned videography with global positioning system (GPS) and geographic information system (GIS) technologies for distinguishing and mapping the distribution of these two aquatic weeds in waterways of southern Texas. Field reflectance measurements made at several locations showed that waterhyacinth generally had higher near-infrared (NIR) reflectance than associated plant species and water. Hydrilla had lower NIR reflectance than associated plant species and higher NIR reflectance than water. Reflectance measurements made on hydrilla plants submerged below the water surface had similar spectral characteristics to water. Waterhyacinth and hydrilla could be distinguished in color-infrared (CIR) video imagery where they had bright orange-red and reddish-brown image responses, respectively. Computer analysis of the imagery showed that waterhyacinth and hydrilla infestaions could be quantified. An accuracy assessment performed on the classified image showed an overall accuracy of 87.7%. Integration of the GPS with the video imagery permitted latitude/longitude coordinates of waterhyacinth and hydrilla infestation to be recorded on each image. A portion of the Rio Grande River in extreme southern Texas was flown with the video system to detect waterhyacinth and hydrilla infestaions. The GPS coordinates on the CIR video scenes depicting waterhyacinth and hydrilla infestations were entered into a GIS to map the distribution of these two noxious weeds in the Rio Grande River

Image analysis and machine learning based methods for disease detection in soybeans

Plant phenotyping is important for genetic enhancements and plant biology research. There is a lot of work done to improve yield of crop plants, by selecting good genotypes to cross-breed in an effort to curb diseases or genetic deficiencies in these crops. In order to select these genotypes, one would have to perform phenotyping. Currently, plant phenotyping is based on visual assessment, where a breeder or researcher would have to visually inspect each plant and visually rate them. Visual rating is inefficient and can be inconsistent due to intra-rater repeatability or inter-rater reliability issues leading to incorrect visual scores. Not only that, it is also labor intensive and time consuming. Hence, there is a need to develop new tools amenable to high throughput phenotyping (HTP) for large scale plant genotype assessments. This requirement for high throughput phenotyping is applicable in a variety abiotic and biotic stresses. We developed a HTP framework which utilizes digital images in an effort for disease detection. This framework enabled us to accurately assign disease ratings to soybean plants that were affected by iron deficiency chlorosis (IDC). Utilizing image analysis techniques, we successfully extracted features pertaining to IDC and trained classification models on these features. A hierarchical classifier, based on linear discriminant analysis and support vector machine classifiers, produced the highest accuracy of 96%. Also, this framework was successfully implemented as a cellphone app. We envision to utilize hyperspectral imaging in the future for more accurate disease detection, prior to symptoms being visible