Pattern Implementation in the Agriculture Domain

Traditionally, farmers spent most of their time observing plant and/or soil conditions and guesstimating the weather conditions before acting – for example, performing irrigation and applying fertilizers. The application of tools and techniques for smart agriculture helps remove this burden from farmers’ shoulders, as decision-making can be performed by analytics engines.

Smart agriculture helps to reduce environmental impact through efficient irrigation and the optimal usage of fertilizer/pesticides. It helps to increase yield by obtaining accurate information about the soil and environmental conditions and then providing information regarding the optimum quantity of input (fertilizers, pesticides, water, etc.) and conditions. The problem with traditional farming is that farmers follow the same procedures regarding sowing, nourishing, irrigation, and harvesting without considering differences that exist in different...

An overview of smart agriculture

This section covers the key terms/definitions that are used in the smart agriculture domain. Understanding these terms is crucial to design and develop smart agriculture-related solutions.

Key terms/definitions

In this section, we will discuss some key technologies associated with smart agriculture:

• Artificial Intelligence (AI): Accurate decision-making is the cornerstone of successful farming, and AI effectively complements/supplements a farmer’s ability to make sound judgments. AI can be used in all stages of crop cultivation – for example, if a crop is infected with a disease, AI provides recommendations to reduce crop wastage. AI is also used to estimate optimal farm input, forecast demand, predict price/yield, identify crops that fit the soil/environmental conditions, and determine the right time to harvest.
• Automation: Automation can be used to start/stop the watering of crops (often remotely), either automatically...

Use cases of IoT in smart agriculture

IoT can be implemented in various ways in the smart agriculture domain. These include the following:

• Soil/crop health monitoring:
  • Measuring temperature, moisture, pH values, and lighting conditions in real time
  • Determining chemical (fertilizer) composition
  • Wind speed/direction sensing
  • Frost detection/avoidance
  • Pest control
  • Climate monitoring and forecasting
  • Comparing crop growth, leaf size, and crop pigmentation with similar crops and conditions
  • Preventing soil degradation
• Enhancing agricultural yield:
  • Predicting the optimum time to plant, irrigate, and harvest crops
  • Predictive maintenance of farm equipment
  • Reducing paperwork while making insurance claims (e.g., crop/livestock damage)
  • Farm asset monitoring
  • Automatic irrigation
  • Aligning irrigation cycles with predicted weather conditions
  • Reducing crop damage via a disease or adverse environmental condition by leveraging predictive analytics
  • Evaluating and neutralizing the effects of the previous season...

Resolving agricultural challenges with a land consolidation platform

Developing countries face unique challenges in the agriculture sector compared to developed economies. Most of the issues stem from the fact that the average landholding is quite small compared to developed countries. For example, India is characterized by a large number of small farms, with the majority of the land dependent on natural rainfall for irrigation purposes. This situation is further compounded by the fact that more than half of the Indian population has farming as a main source of livelihood. A relatively small profit with limited insurance options results in a major chunk of farmers being perennially in debt. Additionally, there is the issue of land fragmentation, as land gets passed from one generation to the next. All these factors result in (constantly) dwindling profits per unit of farmland.

With limited automation, farming demands hard labor. This has resulted in most farmers’ children...

Summary

This chapter provided insights into how IoT plays a role in transforming the agriculture domain. Key challenges inherent in the agriculture sector were listed, and we discussed how IoT (along with other related technologies) can effectively tackle these challenges. Some of the key terms used in smart agriculture were explained. Lastly, a practical problem (smart farm holdings preventing the adoption of smart agriculture practices) prevalent in developing countries was highlighted and a practical solution was proposed.

This and the last few chapters focused on specific domains, and we illustrated how architectural patterns mentioned in the early chapters can be used effectively in these domains. The forthcoming chapters will cover some generic concepts, such as security, analytics, and edge computing. Accordingly, the next chapter will focus on the factors to be considered when selecting sensors and actuators for IoT use cases.