For billions of people worldwide, enhancing the quantity and quality of paddy production stands as an essential goal. Rice, being a primary grain consumed in Asia, demands efficient farming techniques to ensure both sufficient yields and high-quality crops. Detecting diseases in rice crops is crucial to prevent financial losses and maintain food quality. Traditional methods in the agricultural industry often fall short in accurately identifying and addressing these issues. However, leveraging artificial intelligence (AI) offers a promising avenue due to its superior accuracy and speed in evaluation. Nutrient deficiencies significantly impact paddy growth, causing issues like insufficient potassium, phosphorus, and nitrogen. Identifying these deficiencies in paddy leaves, especially during the mid-growth stage, poses a considerable challenge. In response to these obstacles, a novel approach is proposed in this study—a deep learning model.

In Asia, rice is the most consumed grain by humans, serving as a staple food in India. The yield of rice paddies is easily affected by nutrient deficiencies and leaf diseases. To overcome this problem and improve the yield productivity of rice, nutrient deficiency and leaf disease identification are essential. The main nutrient elements in paddies are potassium, phosphorus, and nitrogen (PPN), the deficiency of any of which strongly affects the rice plants. When multiple nutrient elements are deficient, the leaf color of the rice plants is altered. To overcome this problem, optimal nutrient delivery is required. Hence, the present study proposes the use of Fuzzy C Means clustering (FCM) with Improved Tunicate Swarm Optimization (ITSO) to segment the lesions in rice plant leaves and identify the deficient nutrients. The proposed ITSO integrates the Tunicate Swarm Optimization (TSO) and Bacterial Foraging Optimization (BFO) approaches. The Hybrid Convolutional Neural Network (HCNN), a deep learning model, is used with ITSO to classify the rice leaf diseases, as well as nutrient deficiencies in the leaves. Two datasets, namely, a field work dataset and a Kaggle dataset, were used for the present study. The proposed HCNN-ITSO classified Bacterial Leaf Bright (BLB), Narrow Brown Leaf Spot (NBLS), Sheath Rot (SR), Brown Spot (BS), and Leaf Smut (LS) in the field work dataset. Furthermore, the potassium-, phosphorus-, and nitrogen-deficiency-presenting leaves were classified using the proposed HCNN-ITSO in the Kaggle dataset. The MATLAB platform was used for experimental analysis in the field work and Kaggle datasets in terms of various performance measures. When compared to previous methods, the proposed method achieved the best accuracies of 98.8% and 99.01% in the field work and Kaggle datasets, respectively.

Visually impaired people face a lot of difficulties to handle themselves by their own. During walking with a normal cane they sense and guess direction, with the smell in air, counting the footsteps and memorizing their steps and walks and places. Using the modern technologies a cane [7] is designed with advanced techniques to guide them walk and find if there is any obstruction crossing and to wait accordingly and proceed. In this paper will be providing the blind navigation information via audible messages and haptic feedback, helping them localize where are they, where are they going to, and to improve their mobility. Before people were using the Braille dots which would make them tired by frequently pressing the keypads using their hand. With advanced electronic methods being integrated, all the needs of a blind man to walk and live as a normal man would be accomplished.

Rice has become the second major food among rural people. It is one of the most nutrient and low-cost food available in Asia. Rice is composed of two major subspecies as Japonica and Indica and it belongs to the Poaceae family. The World Bank predicts that rice demand will grow 51% faster than population growth in 2025. It is high time to develop a model which predicts the type of infected paddy leaf and avoids the loss of grains. In this paper, the proposed hybrid CNN model, three CNN networks with different layer properties are trained parallel. The extracted feature from these CNN networks is combined to provide the classification of the type of infected leaf. Fieldwork was carried and about 340 samples are collected from five different types of classes. The proposed hybrid model was tested with both the fieldwork dataset and also with an available dataset on the internet. The experimental results show that the proposed model outperforms the other CNN network and yields an accuracy value of 98%, a precision value of 95%, and a misclassification rate of 4% respectively.

The agricultural system has been enhanced in the present era yet an issue of pesticides containing food is making a noteworthy issue for the general population. With the end goal to stay away from this, a framework for the identification of pest was developed to encourage the farmers distinguish the pests, to assist with that an energy efficient routing protocol is utilized so that data can be transferred effectively with less chance of data to be lost in the mid-way. So a simulation has been performed between DSDV, NACRP and LAL algorithm to compare the protocols which are far better in retrieving the data. During this process it has been found that; In DSDV a total of 40% of the data only reaches to the destination whereas in NACRP total of 44% data will only reaches the destination but in the modified LAL algorithm a total of 93% of the data is retrieved. This paper aims to compare the protocols depending on type of routing topology and their performance.