Introduction
Agriculture is a both major industry and the foundation of the economy. Artificial Intelligence (AI) techniques are widely used to solve a variety of problems and to optimize the production and operation processes in the fields of agriculture, food, and bio-system engineering.
Artificial Intelligence Technology
The use of artificial intelligence in the agriculture supply chain is becoming more and more important while involving Artificial Intelligence ML algorithms. The main four clusters are preproduction, production, processing, and distribution.
Preproduction
In fact, in the preproduction, ML technologies are used, especially for the predictions of given features.
· Crop yield
· Soil properties
· Irrigation requirements
Production
In the production phase, the ML could be used for disease detection and weather prediction. There are numerous parameters that affect and play a key role in this phase. Among them are the weather forecasts (sunlight, rainfall, humidity, etc.), crop protection against biotic stress factors (weeds and pathogens) and abiotic stress factors (nutrient and water deficiency), crop quality management, and harvesting. Many different ML algorithms are used to perform prediction and detection in the agriculture sector. Let’s discover by each production phase.
Weather prediction — ANN, deep learning, decision tree, ensemble learning, and instance-based learning
Crop protection — clustering, regression, ANN, and deep learning
Weed detection — ANN, decision tree, deep learning, and instance-based learning
Crop quality management — clustering and regression
Harvesting — deep neural networks, data mining techniques such as k mean clustering, k nearest neighbor, ANN, and SVM
Processing
Concerning the third cluster of the processing phase, utilization of ML approaches is applied, especially to estimate the production planning to reach a high and safe quality of the product. There are many types of processing techniques of agriculture products such as heating, cooling, milling, smoking, cooking, and drying. The choice of effectively combined parameters in the processing stage results in high quality and quantity of a product and, at the same time, avoiding overutilization of resources. To achieve this goal, several food industries use modern food processing technologies by installing software algorithms based on ML. Among the used ML algorithms, there are genetic algorithms, deep learning models, ANN, clustering, and Bayesian network.
Distribution
ML algorithms could be used also in the distribution cluster, especially in storage, transportation, and consumer analysis. The distribution cluster is the final phase in the agriculture supply chain. This stage is the connection between production and processing and the final use or final consumer. ML algorithms could be used in storage, transportation, consumer analytics, and inventory management. In transportation and storage steps, the mainly used algorithms are genetic algorithm, clustering, and regression. These predictive techniques aim to better preserve the food product quality, ensure safe products, and minimize product damage by tracing the product. For consumer analytics, ML techniques such as deep learning and ANN are used in the retailing phase for predicting consumer demand, perception, and buying behavior.
Artificial Intelligence in drones for agriculture
The agricultural drone market is expected to grow from a $1.2 billion industry in 2019 to $4.8 billion in 2024, whereas the robot and agriculture drone market is projected to reach USD 23 billion by 2028. Current drone technologies are more effective in monitoring large monocultural field patterns. Drone monitoring programs, as they stand, have a hard time recognizing areas with increased crop diversity, less well-known produce, and grains that look similar throughout their growth stages. Agricultural drones are now able to supply water, fertilizers, herbicides, and pesticides, and even film, capture images and generate maps in real-time of plants and fields to help farmers make management decisions. Improving Artificial Intelligence (AI) in drones is important to be able to make them more useful to smaller farmers in developing nations.
Conclusion
AI-driven technologies are emerging to help improve efficiency and to address challenges facing the industry including, crop yield, soil health, and herbicide resistance. Agricultural robots are poised to become a highly valued application of AI. The agricultural industry faces various challenges such as a lack of effective irrigation systems, weeds, issues with plant monitoring due to crop height, and extreme weather conditions. But the performance can be increased with the aid of technology and thus these problems can be solved. AI Technology is the key to reduce the challenges in terms of quality and growth of the product.
