Agriculture is the source of life for many living in India. The AI developments have been amending how cultivation has been carried out. AI training requires technical and educational investments in the agriculture sector. The knowledge of farmers regarding the field needs to be translated into AI training. Faster R-CNN and cloud computing systems can detect and classify crop pests. The image recognition system successfully classified five classes of well-known crop pests.  

The main project of using AI is adding new agricultural pest classes with recommended pesticides for specific crops. Computer vision is the latest technology to meet the global food demands by controlling insect pests through AI algorithms. This will increase food security in the long run. Different research institutes have developed several mobile apps based on AI for various crops to identify and manage the insect pest of the crop efficiently. 

Rice, the staple diet of over half of the world’s population, is grown by over 145 million in more than 110 countries and occupies almost one-fifth of the total world cropland under cereals. However, the Rice crop is being attacked by nearly 800 species of insects worldwide, of which some cause severe losses all over India.  

Role of AI 

Computer Vision is good at spotting disorders in agriculture but can also help prevent them. For example, unmanned Aerial Vehicles (UAVs) powered by CV make it possible to automate the spraying of pesticides uniformly across a field. With real-time recognition of target spraying areas, UAV sprayers can operate with high precision in terms of the area and amount to be sprayed. This reduces the risk of contaminating crops, humans, animals and water resources and helps in efficient pest management. 

Mentioned following are some of the commonly found pests in rice crops: 

• Stem Borers: They are the most severe pests of rice worldwide, occur and infect plants from the seedling stage to maturity.  
• Leafhoppers and Plant hoppers: They are sucking insects that remove plant sap from the tissues of the plant 
• Grain Sucking Insect: Their presence in a rice field is detected by foul odor.  
• Defoliators and leaf feeders: The majority of insects belonging to several insect orders feed on rice leaves 
• Root Feeders 

Agriculture using AI 

In agriculture, AI advancements prove improvements in gaining yield and increasing and developing increasing crops. For example, an AI algorithm predicts the time it takes for products like a tomato to be ready for picking, increasing farming effectiveness. Predictive analysts and agricultural robots use sophisticated algorithms and information collected to maintain and monitor crops’ health. The growth of demand for food in the future due to the larger population requires no less than 75% Raine in production to maintain this demand from agriculture.  

Several programs make use of ML algorithms. Computational statistics is what ML is said to be. The field benefits from studying mathematical optimization and theory, which provides methods and application domains. Data science is related research that focuses on unsupervised learning for exploratory data analysis. Predictive analytics is a term used to describe ML algorithms. 

Deep learning is yet another algorithm that can detect the existence of pests with great accuracy. CART, an ML algorithm, can reliably forecast the likelihood of potential illness and pest attacks. Regular human monitoring is unable to predict the severity of pests' issues reliably. Neural Networks Algorithm, confusion matrix and image processing algorithms are other methods used in agriculture.  

Agriculture is the backbone of the Indian economy and will maintain for a long time. It would be able to sustain nearly 17% of the world’s people by using just 2.3% of the world’s land area and 4.2% of the world’s water supplies. Economic reforms implemented in the country during the first decade of the 90s have improved growth. Using AI and related algorithms will contribute to the advanced growth of Indian agriculture. 

Source:

Krishijagran

RSP Science Hub