Agriculture automation is the primary concern and emerging subject for every country. The world's population is increasing very fast, and with the increase in population, the need for food is increasing briskly. Traditional methods farmers use aren't sufficient to serve the increasing demand, so they have to hamper the soil by intensifying their use of harmful pesticides. This affects agricultural practice a lot, and in the end, the land remains barren with no fertility. 

A study by researchers from Gandhinagar Institute of Technology, Gujarat; Lalbhai Dalpatbhai College of Engineering, Gujarat; Vishwakarma Government Engineering College, Gujarat; and School of Technology, Pandit Deendayal Petroleum University, Gujarat, talks about different automation practices like IOT, Wireless Communications, Machine learning and Artificial Intelligence, Deep learning. Some areas are causing problems in the agriculture field, like crop diseases, lack of storage management, pesticide control, weed management, lack of irrigation and water management, and all these problems can be solved by the above-mentioned techniques.

Today, there is an urgent need to decipher issues like the use of harmful pesticides, controlled irrigation, pollution control, and environmental effects in agricultural practice. Automation of farming practices has proved to increase soil gain and strengthen soil fertility.

Automation in Agriculture

Artificial neural networks have often been incorporated into agriculture due to their advantages over traditional systems. The main benefit of neural networks is that they can predict and forecast based on parallel reasoning. Instead of thoroughly programming, neural networks can be trained. It is imperative for any sector to evolve with time. The agriculture sector had to adapt to the breakthroughs and inventions that came along in the automation field. 

Automation in the agriculture sector is a must, and it can be implemented in many ways. Irrigation is the foremost area where automation is necessary for optimal water usage. A soil moisture sensor helps monitor the moisture level of the soil and starts watering the farm as the value falls below the threshold level set by the farmer. The embedded system and the Internet of Things help develop a compact system that monitors the water level of the farm without human interaction.

We can implement many different techniques, such as automation, through different forms, such as machine learning, artificial intelligence, deep learning, neural networks, and fuzzy logic. The idea is to use any of these extended methods to reduce human intervention and human efforts. All these methods have their advantages and disadvantages, but how they are used differentiates them from each other. The meagre research in the deep learning technique analyses the dataset of images from the past data fed and classifies the plants or flowers. 

Deep learning application is required in this field as it provides a significant impact on modern techniques; it extends Machine learning by adding more depth to the model. The main feature of deep learning is the raw data process, which increases accuracy and classification. Plant recognition, fruit counting, and predicting future crop yield are the primary targets for implementing deep learning. Large datasets of images are required to train the model, while some techniques use text data to train the model.

The future scope

According to the study, the young farmers will invest more in automation with much interest than the older farmers. The new technology has to be introduced slowly with time. Slowly, the agriculture sector is moving towards precision farming, in which management will be done on the basis of individual plants. Deep learning and other extended methods are used to detect the plant or flower type; this will help farmers provide a favourable environment for the plant for sustainable growth. Eventually, the production of more customized fruits and plants will grow, which will increase the diversity of products and production methods. 

Artificial intelligence techniques are growing rapidly and can be used to detect diseases of plants or any unwanted weed on the farm by using CNN, RNN or any other computational network. The drones monitor the farm and give continuous real-time data of the field so that the farmers can know in which field the water quantity is less and can only start irrigation in that area. This will prevent water flooding or scarcity in the field, and the crops will get more water. Many different integrated approaches can provide a viable environment and increased growth. 

Full Study