Deep Convolutional neural network (CNN) in tea leaf chlorophyll estimation: A new direction of modern tea farming in Assam, India

This study presents the uprising of leaf chlorophyll estimation from traditional mechanical method to machine learning-based method. Earlier chlorophyll estimation techniques such as Spectrophotometer and Soil Plant Analysis Development (SPAD) meter demand cost, time, labour, skill, and expertise. A small-scale tea farmer may not afford these devices. The present study reports a low-cost digital method to predict the tea leaf chlorophyll using 1-D Convolutional Neural Network (1-D CNN). After capturing the tea leaf images using a digital camera in a natural light condition, a total of 12 different colour features were extracted from tea leaf images. A SPAD was used to estimate the original chlorophyll value of the tea leaves. The paper shows the correlation of original tea leaf chlorophyll with the extracted colour features of the tea leaf images. Apart from 1-D CNN, the Multiple Linear Regression (MLR) and K-Nearest Neighbor (KNN) were also applied to predict the tea leaf chlorophyll and compared their results with the 1-D CNN. The 1-D CNN model outperformed with an accuracy of 81.1%, Mean Absolute Error (MAE) of 3.01, and Root Mean Square Error (RMSE) of 4.18. The investigation system is very simple and cost-effective. It can be used in tea farming as a digital SPAD for faster and accurate leaf chlorophyll estimation in an easy way

A study on livelihood activities followed by the male rural youths in flood affected Dhemaji district of Assam state of India

The study was conducted at flood affected Dhemaji district of Assam where the flood is a major concern for the livelihood of rural male youth. The paper focused on existing livelihood activities opted by of rural male youths, their reason for choosing the activities and problems encountered in doing it. To date, no study has looked specifically at all the three aspects. The present study together investigated all the three elements. The study selected 200 rural male youths from the district as respondents following multistage random sampling. The survey found the mean age of the respondents as 28.83 years. The study found that the respondents opted 17 types of livelihood activities. Most (25.50%) of the interviewees chose sali rice cultivation, and they (100.00%) cited the availability of suitable land as a reason. Under vegetable cultivation, 96.07% respondents mentioned scope for round the year production and income generation as reasons. The respondents opted piggery(100.00%), poultry (100.00%), weaving (100.00%), dairy (92.85%), goatery (91.66%) and fishery (90.00%) mentioned high market demand as a reason. Regarding the problem, all respondents (100.00%) opting sali rice cultivation and vegetable cultivation reported flood, flood-induced sand and insufficient irrigation as problems. The high price of improved livestock breed was an issue for respondents (100.00%) opting dairy, poultry, and piggery. In fishery, 80.00% respondents mentioned nonavailability of quality fingerlings as a problem. The paper urges the policy makers, researchers and development organisations for utilising the findings to select appropriate interventions for providing livelihood security to male rural youths of Dhemaji district of Assam State, India

Double Stage chain routing Protocal in WSN

Wireless sensor networking is most popular fields in today’s world. In this paper, we have discussed the different energy optimization protocols of WSN. We have forwarded a new protocol “Double Stage Chain Routing Protocol†from WSN. Our main focus is on extending the residual energy and network’s life time at least more than LEACH, CCM and TSCP. The result of our protocol is represented with the help of graph with comparison with TSCP and it is found that DSCRP gives better network lifetime than LEACH. The proposed algorithm is acceptable in the network lifetime of DSCRP as well as in network life time

Predication of soil pH using HSI colour image processing and regression over Guwahati, Assam, India

Soil is known to be the most valuable natural source for all agriculture fields. Soil has two properties, namely- physical and chemical. These properties include soil moisture, texture, etc. and the latter include pH value. Soil texture plays an important role in crop cultivation. The physical properties of soil such as texture and granular size determine the water and nutrient holding capacity. Also the chemical property like pH value is very important for plant growth and development. Soil having pH value between 5.5 and 7 is optimal for agricultural purpose. Hence, a detailed study of soil pH property is necessary for cultivation. But laboratory method of soil pH calculation is a very costly and tedious process. Therefore, it is essential to develop an expert-based system that will overcome this issue. However, the system must be able to give correct result and should match with those conducted in laboratory. Farmers analyze pH either in lab or by soil pH card based on soil image color. But this is not an effective method since it relies heavily on human perception. Hence, we have developed an expert based system which can determine the pH of the soil without any human error. For this, we have conducted our experiments with the help of MatLab tool and smart phone as we have concerned about the rural farmers. We have analyzed and compared the proposed system results with the traditional laboratory methods with regression and have found 86 % accuracy in our model

Unsupervised Extractive News Articles Summarization leveraging Statistical, Topic-Modelling and Graph-based Approaches

Due to the presence of large amounts of data and its exponential level generation, the manual approach of summarization takes more time, is biased, and needs linguistic professional experts. To avoid these substantial issues or to generate a succinct summary report, automatic text summarization is very much important. Three different approaches namely the statistical approach such as Term Frequency Inverse Document Frequency(TF-IDF), the topic modeling approach such as Latent Semantic Analysis (LSA), and graph-based approaches such as TextRank were applied to generate a concise summary for the benchmark the British Broadcasting Corporation (BBC) news articles summarization dataset. The domain-specific implementations of each approach in the five domains of the dataset and domain-agnostic prospects were explored in the paper while drawing various insights. The generated summaries were evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) framework, leveraging precision, recall, and f-measure metrics. The approaches were not only able to achieve a commendable ROUGE score but also outperform the previous works on the datase

Smartphone assist deep neural network model to recognize the high-quality tea using leaf maturity and its effect on leaf chlorophyll

Immature and tender tea leaves always produce high-quality tea than mature tea leaves. Depending on the maturity and age of the leaf, the colour and texture of the tea leaf are different. The photosynthesis capacity of the tea leaf also changes with the change of leaf maturity. Though the tea farmer plucks, classifies, and recognizes the best tea leaves (immature and tender) by viewing the visual symptoms and position of the leaves, the method is not authentic all time and leads to the overall degradation of the tea quality. The present study presents a smartphone assist tea leaf recognition system by analyzing the colour and texture properties of the tea leaf. The six different colour features and 4 Haralick texture features were extracted in the colour and grey domain of the leaf images. Three types of tea leaves, i.e., mature, immature, and tender, were classified using Deep Neural Network (DNN) with ADAM (Adaptive Moment Estimation) optimizer. With an accuracy of 97%, the DNN outperformed the Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The SVM and KNN reported a total of 94.42% and 95.53% accuracy, respectively. The investigated system using DNN with an average precision and recall value of 98.67 and 98.34, respectively, may detect and classify the tea leaf maturity status. The system also can be used in AI-based tea plucking robotic systems or machines

Smartphone assisting convolutional neural networks for soil texture classification in dry and wet humid conditions in West Guwahati, Assam

Soil texture using a hydrometer or pipette method requires expertise, although these are accurate. A soil expert may help the farmer to detect the soil texture by analyzing the visual texture of the soil, which is not always accurate. This paper presents the smartphone image-based sand and clay soil classification in wet and dry humid conditions using Self Convolution Neural Network (SCNN) and finetuned MobileNet.A soil dataset of 576 soil images was prepared using a low-cost smartphone under natural light conditions. Different augmentation techniques such as shift, range, rotation, and zoom were applied to the soil dataset to increase the number of images in the soil dataset. The best performance of the MobileNet was reported at epoch 15 with a testing and training loss of 0.0091 and 0.0194, respectively. Though the SCNN model performed best at epoch 10 with a testing accuracy of 99.85%, the MobileNet reported less computation time (167.8s) than the SCNN (273.2s). The precision and recall of the models were 99.62 (MobileNet) and 99.84 (SCNN). The accuracy of the SCNN reported itself as the best model, whereas the computing time of the MobileNet reported itself as the best model in different humid conditions. The model can be used to replicate the traditional soil texture analysis method and the farmers can use it for better productivity

Performance analysis of support vector machine for early identification of citrus diseases

Early citrus disease detection is necessary for optimum citrus productivity. But detecting a citrus disease at an early stage requires expert views or laboratory tests. But getting an expert view of all time is impossible for rural farmers. The present study aimed to create a low-cost, intelligent, affordable citrus disease classification system. This study offered a Support Vector Machine (SVM) based smart classification method for categorizing various citrus diseases. Citrus photos were subjected to a variety of image processing techniques to categorize the diseases using SVM and the kernel. Prior to classification, the images were segmented and the hue channel threshold value was used to differentiate the diseased area from the remaining portion of the image. The segmented image’s color and grey domains were used to extract 13 different texture and color features. This study outlined three different SVM kernel types- Linear, Gaussian, and Polynomial, and evaluated their accuracy and confusion matrix performances. The Radial Based Function with a polynomial kernel derived from the SVM outperformed the SVM's linear and Gaussian kernel

Unsupervised Extractive News Articles Summarization leveraging Statistical, Topic-Modelling and Graph-based Approaches

952-962Due to the presence of large amounts of data and its exponential level generation, the manual approach of summarization takes more time, is biased, and needs linguistic professional experts. To avoid these substantial issues or to generate a succinct summary report, automatic text summarization is very much important. Three different approaches namely the statistical approach such as Term Frequency Inverse Document Frequency(TF-IDF), the topic modeling approach such as Latent Semantic Analysis (LSA), and graph-based approaches such as TextRank were applied to generate a concise summary for the benchmark the British Broadcasting Corporation (BBC) news articles summarization dataset. The domain-specific implementations of each approach in the five domains of the dataset and domain-agnostic prospects were explored in the paper while drawing various insights. The generated summaries were evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) framework, leveraging precision, recall, and f-measure metrics. The approaches were not only able to achieve a commendable ROUGE score but also outperform the previous works on the dataset

<i>ViT-SmartAgri</i>: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture

Invading pests and diseases always degrade the quality and quantity of plants. Early and accurate identification of plant diseases is critical for plant health and growth. This work proposes a smartphone-based solution using a Vision Transformer (ViT) model for identifying healthy plants and unhealthy plants with diseases. The collected dataset of tomato leaves was used to collectively train Vision Transformer and Inception V3-based deep learning (DL) models to differentiate healthy and diseased plants. These models detected 10 different tomato disease classes from the dataset containing 10,010 images. The performance of the two DL models was compared. This work also presents a smartphone-based application (Android App) using a ViT-based model, which works on the basis of the self-attention mechanism and yielded a better performance (90.99% testing) than Inception V3 in our experimentation. The proposed ViT-SmartAgri is promising and can be implemented on a colossal scale for smart agriculture, thus inspiring future work in this area