Intelligent Data Mining and Fusion Systems in Agriculture

Xanthoula-Eirini Pantazi, Dimitrios Moshou, Dionysis Bochtis

• Publisher: Academic Press
• Year: 2019
• ISBN:
  9780128143926
• Topic: Business
• Subtopic: Agribusiness
• Index: Business

Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. Sections cover the combination of sensors with artificial intelligence architectures in precision agriculture, including algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions. This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms.

• Covers crop protection, automation in agriculture, artificial intelligence in agriculture, sensing and Internet of Things (IoTs) in agriculture
• Addresses AI use in weed management, disease detection, yield prediction and crop production
• Utilizes case studies to provide real-world insights and direction

Chapter 1

Sensors in agriculture

Abstract

According to the current trends of increased sustainability concerns in production systems, there is a high need for the targeted audience to become aware of the connection between decision making in agricultural operations and the decision support features that are offered by advanced computational intelligence algorithms combined with sensor fusion from a variety of sensors that are capable of providing a better view for crop condition and simultaneously lay the foundation for efficient crop management in agriculture. The main objective of the current book is to present methods of Computational Intelligence and Data Fusion with application in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. The presented methods are related to the combination of sensors with Artificial Intelligence architectures in Precision Agriculture. The Artificial Intelligence algorithms included Bio-inspired Hierarchical Neural Maps and Novelty Detection algorithms capable of detecting sudden changes in different conditions.

Keywords

Precision farming; Artificial neural networks (ANNs); Crop monitoring; Hierarchical self organizing; Maps novelty detection

Contents

• 1.1 Milestones in agricultural automation and monitoring in agriculture
  • 1.1.1 Introduction
  • 1.1.2 Sensing systems for PA
  • 1.1.3 Benefits through sensing for PA farming systems
  • 1.1.4 Current trends in agricultural automation
  • 1.1.5 Current challenges of PA sensing applications
• 1.2 Sensors for soil analysis and characteristics
  • 1.2.1 Visible (VIS)/NIR spectroscopy
  • 1.2.2 Soil sensing using airborne and satellite imaging
• 1.3 Yield sensing
• 1.4 Sensors for weed management
• 1.5 Sensors for disease detection and classification
• References
• Further reading

1.1 Milestones in agricultural automation and monitoring in agriculture

1.1.1 Introduction

By 2050, is expected that world population will reach 9.8 billion according to a new launched United Nations report (www.un.org). Consequently, as the global demand for food and agricultural crops elevates, novel and sustainable approaches are needed that employ agricultural technologies focusing not only on agricultural activities for crop production but also on the global impacts concerning the appropriate nitrogen fertilizer use, reduced GHG emissions and water footprints.

The intensification of agricultural activities, mechanization, and automation throughout the years are regarded the main reasons that contributed significantly to the rise of agricultural productivity. This led to the evolution of autonomous farming systems for field operations, livestock management systems and growth control systems oriented mostly on greenhouse monitoring, climate control and irrigation management systems. The efficient agricultural production management enables the decrease of negative environmental impacts allowing both efficiency, as well agricultural products safety.

Agriculture has to meet significant challenges in the face of providing technical solutions for increasing production, while the environmental impact decreases by reduced application of agro-chemicals and increased use of environmental friendly management practices. A benefit of this is the reduction of production costs. Technologies of sensors produce tools to achieve the above-mentioned goals. The explosive technological advances and development in recent years enormously facilitates the attainment of these objectives by removing many barriers for their implementation, including reservations expressed by farmers themselves. Precision Agriculture is an emerging area, where sensor-based technologies play an important role. Farmers, researchers, and technical manufacturers, all together, are joining efforts to find efficient solutions and improvements in production and in to reductions in costs.

Precision agriculture (PA) is based on the idea of sensing for monitoring and deploying management actions according to spatial and temporal crop variability. Therefore, sensor technologies are regarded as fundamental components of PA systems. PA technologies combine sensors, farm management information systems, compatible machinery that can apply inputs according to map requirements, for production optimization. Through adaptation of PA production inputs, an efficient and precise resources management for protecting the environment while maintaining the sustainability of the food supply is achieved.

Precision agriculture comprises a powerful tool for monitoring food production chain and securing both agricultural production quantity and quality by employing novel technologies to optimize the agricultural operations for producing simultaneously hiring yield and quality with site-specific input. More specifically, sensor-based monitoring technologies give precise yield predictions and early alerts concerning the crop condition. PA makes farming more credible by improving registration of operations, by monitoring and documenting. Crop monitoring provides more precise predictions on agricultural products quality, making consequently the food chain easier to be monitored for stakeholders (producers, retailers, customers). It is also capable of giving vital information for plant health condition. Recent technologies are able to monitor plants and crops at different scales of resolution. The monitoring scales vary from field (ca. 30 × 30 m) to plant monitoring (ca. 30 × 30 cm). It is expected that the upcoming technologies will be capable of making leaf level (ca. 3 × 3 cm) and symptoms on leaves (ca. 0.5 × 0.5 cm) detectable by sensor based optical diagnostics. On the other hand, diseases untraceable by conventional means will be detected by automated optical sensing and optimal planning options.

1.1.2 Sensing systems for PA

Crop motoring is not a new tendency. The first approaches for crop monitoring are dated back in ancient Egypt, where attempts to examine the River Nile water level fluctuations effect on crop yield have been indicated (Luiz, Formaggio, & Epiphanio, 2011). The above measurements were employed to contribute not only to the tax management system but also to the prevention of famine. Nowadays, it is more than necessary to use the intelligent tools of agriculture to meet the multiple social needs that have arisen concerning trustworthy crop product information which consequently are capable of guaranteeing and assessing crop and food safety (Becker-Reshef et al., 2010).

It should be stressed that sensing monitoring systems estimations in agriculture are crucial to be provided at an early stage during the growing period and updated regularly until harvesting so as timely vital information for crop production, condition and yield productivity both at the (sub)regional to the national level are provided. Consequently, this information are combined by forming early warning systems, which are capable of providing homogeneous data sets. The statistically valid precision and accuracy data gives the opportunity to stakeholders, to recognise and discriminate areas, which vary a lot in terms of production and productivity, and to take early decisions. For example, a synergy between a satellite based remote sensing approach and modeling tools can offer timely useful and spatial detailed information for crop status and yield with reasonable costs (Roughgarden, Running, & Matson, 1991).

However, sensing monitoring of agricultural production often conveys some additional limiting-factors closely related to the ability of unfavorable growing conditions altering within short time periods (FAO, 2011) including the seasonal patterns associated with crops biological lifecycle, the physical landscape, the management practices and the climate variability. Food and Agriculture Organization (FAO) (2011), stresses the contribution of early and accurate information that concerns crop status and yield productivity to underlying agricultural statistics and to the decision making process of the associated monitoring systems. On the contrary, information is not considered of high value if it is available too late. Considering variables space and time sensitivity and crops perishable nature, it is concluded that agricultural monitoring systems are vital to be timely assessed.

1.1.3 Benefits through sensing for PA farming systems

The positive effects of PA farming systems adaptation in agricultural activities are reflected mainly in two areas including the farmers’ profitability and the protection of the environment. For achieving profitability, it is important that the cost of investing in such promising sensing technologies is fully focused on some envisioned reasonable return. PA farming systems allows detailed agricultural production tracking and tuning by enabling farmers to modify and timely program the fertilization and agrochemicals application according the field's spatial and temporal variability. By adapting PA technologies, the farmers are given the opportunity to manage, their own agricultural machinery in a more precise and efficient way to meet the cultivated crop needs. Moreover, PA systems are capable of forming a database of many years, which enables the combination of both yield and weather data for adjusting crop management and predicting crop productivity. Agricultural machinery movements and their work recordings, also contribute to the creation of a useful database tool able to assess the duration of several farming activities. Based on the crop yield variability in a field and the inputs cost, farmers are enabled to assess the economic risk and consequently estimate the cash return over the costs per hectare in a more precise and trustworthy w...