From Industry 4.0 to Agriculture 4.0: Shifting towards an intelligent agricultural decision-support system based on machine learning.

Co-author: Dr Gu Pang

Introduction 

Progress in the agricultural industry depends on strategic investments in technologies. Cutting-edge technologies such as the Internet of Things (IoT), robotics, sensors, big data, cloud computing, and artificial intelligence (AI) are propelling the agricultural sector toward the Agriculture 4.0 paradigm, elevating productivity, innovation, and sustainability to new heights [1], [2], [3], [4]. Machine Learning (ML) is a subset of AI that involves the use of digital machines capable of making decisions based on multiple data inputs and algorithms. It can learn and perform specific tasks automatically without explicit programming and can achieve significant accuracy in these tasks due to the large amounts of data [4], [5]. ML holds great potential for enhancing various aspects of Agriculture 4.0. 

Agriculture 4.0: What Is It? 

Early-stage Agricultural Revolutions

In traditional farming practices (Agriculture 1.0), farmers relied heavily on indigenous tools for cultivation and made decisions mostly based on their own experience. This mode of production required a great deal of manual labor despite very low productivity [6], [7]. Later, in Agriculture 2.0, agricultural machinery and assembly line-based mass production were introduced into the agricultural sector, replacing home-based animal husbandry with large-scale intensive farming [6], [7]. Meanwhile, new energy sources, along with developments in the information and transportation industry, made long-distance shipment possible. Agriculture 3.0 benefited from software engineering, information, and communication technologies, which allow precision agriculture [7].

Agricultural 4.0 

The initial three stages of the revolution brought significant enhancements to the form and efficiency of agricultural activities. However, challenges persist, including issues related to food safety, carbon emissions, and supply chain shortages. Agriculture 4.0 is characterized by the integration of cutting-edge technologies, enabling a high degree of automation, real-time farm management, and data-driven intelligent decision-making. These revolutions notably enhance the efficiency, productivity, safety, and environmental performance of the agricultural sector. Additionally, they underpin emerging development trends such as vertical farming, circular agriculture, digital agriculture, and aquaponics [6], [7]. 

How Machine Learning is Used in Agriculture 4.0

Machine learning is a subset of artificial intelligence that uses statistical techniques to enable machines to imitate intelligent human behavior [4], [5]. Instead of receiving explicit programming instructions, ML algorithms learn to make predictions and decisions by automatically processing data. These algorithms adapt over time based on new data and experiences to improve efficacy and efficiency. ML is capable of correcting, autocalibrating, or rebuilding itself if it detects data drift [4], [8]. These features of ML open up a new era for crop and livestock management in agriculture. 

Yield Prediction  

Yield prediction plays a critical role in precision crop management, facilitating yield mapping and aligning crop supply with demand [8]. ML employs both descriptive and predictive analytics algorithms, offering a cost-effective, efficient, and non-destructive means for yield prediction. By leveraging real-time monitoring data on surface weather, soil conditions, physico-chemical parameters, and color digital images of corps, the ML system provides farmers with comprehensive yield-related information. This empowers them to optimize the allocation of production resources and manpower [9], [10]. 

Disease Detection 

Pest and disease control are vital in agriculture, especially in greenhouse conditions, as they directly impact yield and product quality. A common practice is to uniformly spray pesticides over the cropping area. However, pesticide usage has become a significant concern due to its implications for product safety and potential side effects, such as groundwater and soil contamination, which can affect the local ecosystem [11]. In the literature, ML algorithms based on data collected through red-green-blue (RGB) images has been utilized for the automatic detection of pests and diseases [11], as well as for precise input of agrochemicals in terms of timing and placement [12]. Meanwhile, deep learning methods such as Convolutional Neural Networks (CNNs) are employed to classify, for instance, healthy plants from infected ones [13]. 

Corp Quality Control  

ML has also been employed to identify and classify f features associated with product quality, aiding in price enhancement and waste reduction. Quality parameters such as flesh firmness, color, and height are gathered through RGB images or X-ray images to detect crop quality or seed germination potential [14], [15], [16]. Additionally, ML techniques provide precise estimation and control of soil drying, condition, moisture, and temperature, enabling automatic decisions regarding watering or fertilizing, which are critical for crop quality [6], [8]. 

Livestock Production  

ML models based on data collected by various types of motion capture sensors (e.g., pedometers, collar sensors with magnetometers, and three-axis accelerometers) and monitoring systems are applied to identify and classify certain animal behaviors such as estrus, dietary changes, chewing patterns, idleness, and rumination automatically [8], [17]. Physical and growth characteristics, including the animal’s age, weight, metabolites, backfat and muscle thickness, and biometric traits, are monitored to ensure health and product quality [18]. Moreover, ML is utilized for accurate estimation and prediction of farming parameters to enhance the economic efficiency of the production system [6], [8]. 

References 

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