AI revolutionizing early disease detection in protected crops
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Kshetrimayum Somendro Singh and Dr Aruna Beemrote
Protected cultivation systems like greenhouses and polyhouses provide a controlled environment for growing high-value crops like tomato, capsicum, cucumber, and ornamental plants like flowers, etc. These systems help in improving yield and quality of agricultural crops by regulating temperature, humidity, light, and irrigation. However, the enclosed conditions that favour crop growth can also promote the rapid spread of plant diseases if not detected early. Diseases such as powdery mildew, downy mildew, and botrytis can quickly become severe, leading to significant economic losses.
Traditional methods of detecting plant diseases mostly depend on farmers or field experts visually examining crops. This process takes time and often varies from person to person, making it less reliable. In many cases, diseases are noticed only after clear symptoms appear, by the time some damage has already occurred. Recently, Artificial Intelligence (AI) has started to offer a better alternative. AI-based systems can process large volumes of data, identify patterns, and detect very small changes in plants that the human eye cannot notice. Because of this ability, AI is proving to be highly useful for detecting diseases at an early stage, especially in protected cultivation systems.
Concept of AI in Plant Disease Detection
Artificial Intelligence (AI) refers to the ability of machines to perform tasks that normally require human intelligence, such as learning, reasoning, and decision-making. AI uses techniques such as machine learning (ML) and deep learning (DL) to analyze images, environmental data, and plant physiological signals to detect and diagnose plant diseases.
Machine learning algorithms are trained on data sets that include healthy and diseased plant samples. Over time, these algorithms learn to distinguish between normal and abnormal plant conditions. Deep learning, especially Convolutional Neural Networks (CNNs), has shown high accuracy in image-based classification and pattern recognition, which will help in differentiating and diagnosing the disease. These models can identify disease symptoms such as leaf spots, discolouration, and lesions even at early stages. In protected cultivation, AI systems are often integrated with sensors and cameras to monitor crop health continuously for real-time disease detection and timely intervention.
Tools and Technologies Used: Image-Based Detection Systems
High-resolution cameras must be installed inside greenhouses to capture images of plant leaves and stems. The collected images are analyzed by using trained AI models to detect specific disease symptoms. CNN-based models have been widely used due to their ability to process complex visual patterns.
(To be contd)
Protected cultivation systems like greenhouses and polyhouses provide a controlled environment for growing high-value crops like tomato, capsicum, cucumber, and ornamental plants like flowers, etc. These systems help in improving yield and quality of agricultural crops by regulating temperature, humidity, light, and irrigation. However, the enclosed conditions that favour crop growth can also promote the rapid spread of plant diseases if not detected early. Diseases such as powdery mildew, downy mildew, and botrytis can quickly become severe, leading to significant economic losses.
Traditional methods of detecting plant diseases mostly depend on farmers or field experts visually examining crops. This process takes time and often varies from person to person, making it less reliable. In many cases, diseases are noticed only after clear symptoms appear, by the time some damage has already occurred. Recently, Artificial Intelligence (AI) has started to offer a better alternative. AI-based systems can process large volumes of data, identify patterns, and detect very small changes in plants that the human eye cannot notice. Because of this ability, AI is proving to be highly useful for detecting diseases at an early stage, especially in protected cultivation systems.
Concept of AI in Plant Disease Detection
Artificial Intelligence (AI) refers to the ability of machines to perform tasks that normally require human intelligence, such as learning, reasoning, and decision-making. AI uses techniques such as machine learning (ML) and deep learning (DL) to analyze images, environmental data, and plant physiological signals to detect and diagnose plant diseases.
Machine learning algorithms are trained on data sets that include healthy and diseased plant samples. Over time, these algorithms learn to distinguish between normal and abnormal plant conditions. Deep learning, especially Convolutional Neural Networks (CNNs), has shown high accuracy in image-based classification and pattern recognition, which will help in differentiating and diagnosing the disease. These models can identify disease symptoms such as leaf spots, discolouration, and lesions even at early stages. In protected cultivation, AI systems are often integrated with sensors and cameras to monitor crop health continuously for real-time disease detection and timely intervention.
Tools and Technologies Used: Image-Based Detection Systems
High-resolution cameras must be installed inside greenhouses to capture images of plant leaves and stems. The collected images are analyzed by using trained AI models to detect specific disease symptoms. CNN-based models have been widely used due to their ability to process complex visual patterns.
(To be contd)