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Bhusan Chettri Explained Automatic Crop and Soil Monitoring Using Artificial Intelligence


In this article, Bhusan Chettri discusses how artificial intelligence (AI) can be used to monitor soil and crop conditions using automatic systems driven by advanced AI algorithms to produce better agricultural products. It describes the steps involved in forming such automatic systems and also details their potential advantages and disadvantages.

“Agriculture is one of the most important sectors for the economy of our country, India. It is the foundation on which the economy of our country is based. The use of artificial intelligence technology in agriculture can increase productivity and efficiency. Artificial intelligence is used for disease identification and detection, precision agriculture and many other applications. Machine learning, a kind of artificial intelligence, allows computers to learn and make decisions on their own. In agriculture, machine learning is used to improve crop yields, reduce costs and increase efficiency. says Bhusan Chettri, an AI researcher who is currently exploring AI applications in agriculture and weather forecasting.

AI can be used for crop and soil monitoring through the use of sensors and machine learning algorithms. Sensors placed in the field can collect data on various parameters such as temperature, moisture levels, soil nutrient levels and sunlight intensity. This data can then be fed into machine learning algorithms, which can analyze the data and provide insight into optimal conditions for crop growth and soil health. From this data, learning algorithms also discover trends and patterns useful for understanding ground conditions. For example, the algorithm can determine the optimal amount of water and nutrients needed by crops and provide recommendations on when and how to apply these inputs. It can also detect potential problems such as pests or diseases and provide early warning to farmers. Additionally, AI can be used to monitor the health of the soil itself, providing insight into its composition and potential issues such as soil erosion or compaction. This can help farmers take the right steps to improve soil health and maintain productivity. The AI ​​system can also alert farmers when soil conditions are not optimal for plant growth and suggest potential solutions. Additionally, AI can be used to monitor crop health and predict potential pest infestations or outbreaks, allowing farmers to take preventative action.

Bhusan Chettri explains that the use of AI technology for crop and soil monitoring is very helpful in getting a lot of information about crop health and soil quality. Technology was used to get the best results in time. Through the use of technology, farmers can easily access all data regarding the status of their crops and other agricultural activities. It helps to save time and money. The technology provides accurate, on-time analysis and helps reduce environmental damage caused by farmers.

There are several free datasets that can be used to train AI (machine learning and deep learning models) for agriculture. For example, the Agricultural Research Service (ARS) of the United States Department of Agriculture (USDA) provides a dataset containing images of various crops and plant diseases, which can be used to train models of machine learning for crop health monitoring and disease detection. The dataset is freely available and can be used to train machine learning models for crop health monitoring and disease detection. The dataset includes images of different crop species, such as corn, wheat, soybeans and rice, as well as images of common plant diseases, such as rust, blight and wilt. Images are tagged with the specific culture or disease, along with additional metadata such as image location and date. The ARS dataset can be used to train deep learning models to recognize different crops and diseases in images, allowing farmers and researchers to monitor crop health and spot potential problems. This can help improve crop yields and reduce the spread of plant diseases. In addition, the Food and Agriculture Organization of the United Nations (FAO) provides a dataset containing information on global food production and consumption, which can be used for predictive modeling and analysis of agricultural trends and challenges.

“In addition, understanding soil texture is very important to ensure healthy agricultural production. In this direction, it is also possible to train an automatic system for fast and accurate soil texture prediction using machine learning and artificial intelligence algorithms,” says Bhusan Chettri. The following steps should be followed for this.

Collect a large data set of ground texture images from a variety of locations and conditions. The dataset should include images of different soil textures, such as sandy, clay, and loam, along with corresponding labels or annotations indicating the soil texture of each image.

Use this dataset to train an AI (machine learning or deep neural network model) to recognize different ground textures in images. This would typically involve using a type of neural network called a convolutional neural network (CNN), which is well suited for image recognition tasks.

Validate the trained model using a separate image dataset to ensure it is accurate and reliable. This would involve comparing the model predictions to the actual ground textures of the images in the validation dataset and measuring the model’s performance using metrics such as accuracy and precision.

Implement the trained deep learning model in a computer vision system, which would be used to automatically capture and analyze images of soil textures in the field. The machine vision system should include hardware components such as cameras and sensors, as well as software components such as image processing algorithms and the trained deep learning model.

Test and evaluate the automated monitoring system in the field to ensure that it is able to accurately and reliably predict soil textures under real-world conditions. This would involve collecting images of the ground textures using the machine vision system and comparing the system’s predictions to the actual ground textures.

Train an AI for automatic crop and soil monitoring

To train an AI (machine learning or deep neural network model) for automatic crop and soil monitoring, you need to gather a large dataset of images and other crop and soil related data. This dataset should include a wide range of examples, such as images of different plant species, growth stages, and soil types, along with corresponding labels or annotations indicating the class or category of each example. One can also use the freely available related datasets discussed earlier. After the data collection is complete, training a neural network model is performed by presenting it with the examples from the dataset and adjusting the strengths of the connections between the neurons in the network based on their performance. This process is known as backpropagation and is usually performed using specialized software and hardware tools. During training, the neural network model would learn to recognize patterns and relationships in data, and it would be able to make predictions or classifications on unseen new data. The accuracy of the model would be assessed using a separate dataset that is not used for training, and the model would be refined and improved based on this assessment. Overall, training a neural network for automatic crop and soil monitoring is a complex and time-consuming process that requires a large data set and specialized tools and expertise. However, the results can be very accurate and valuable for agricultural applications.

There are many examples of research efforts to train automatic crop and soil monitoring systems. Here are some examples. In one study, researchers trained a CNN to identify and classify different crops on satellite images. The CNN was trained using a dataset of over 100,000 crop images, and it achieved over 90% accuracy on a validation dataset. The trained model was then used to automatically map the distribution of different crops in a study area. In another study, researchers used a combination of machine learning algorithms, including random forests and support vector machines, to predict soil moisture levels from remote sensing data. The algorithms were trained using a dataset of soil moisture measurements and satellite images, and they were able to accurately predict soil moisture levels with an average error of less than 5 %. In yet another study, researchers trained a deep learning neural network to classify different soil textures in images. The network was trained using a dataset of over 2,000 soil texture images, and it achieved an accuracy of over 95% on a validation dataset. The trained model was then used to automatically identify and map soil textures in a study area. Taken together, these studies demonstrate the potential of machine learning and deep learning algorithms to automate crop and soil monitoring tasks.

“Overall, the development of an automated monitoring system for fast and accurate soil texture prediction using an image-based deep learning network and a Machine vision is a complex and difficult task that requires expertise in machine learning, computer vision, and soil science. However, the resulting system can provide valuable information for agricultural applications,” says AI, machine learning and data science researcher Bhusan Chettri.



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