Wheat crop classification and prediction are important tasks for the optimization of crop yield and resource utilization. In this study, we propose an Artificial Neural Network (ANN) model integrated with Genetic Algorithm (GA) to predict and classify the wheat crop images of different ages. The dataset of 19,300 images was used, and the model was trained and tested using various performance evaluation metrics. The results show that the proposed ANN+GA model achieved the highest accuracy of 99.29% during the training phase and 98.65% during the testing phase. The model was also compared with other state-of-the-art machine learning models, and the proposed model was found to be superior in terms of accuracy, specificity, sensitivity, precision, and F-measure. The graph of training and testing accurateness and loss values in contradiction ofindividual epoch demonstrating the speediness of model convergence. Our proposed model is feasible and robust, giving better classification and crop forecast outcomes for numerous wheat crop age groups with least resource necessities. These findings could be useful for farmers and agricultural researchers in improving crop yield and resource management.

An innovative smart agriculture system utilizing a deep neural network and embedded system to enhance crop yield

Detection and identification of disease in plants is the fundamental step to avoid various constraints and to enhance productivity. In agricultural farm, the crops are prone to various bacterial and fungal disease. It is initiated due to the varied ecological conditions and soil categories. The earlier prediction of diseases helps to prevent the complete degradation of crops. The disease is detected through small patches on the leaves of the plant. The evolution of automation and innovations helps in the earlier detection of disease in the leaves and fruits of the crops. It is achieved by artificial intelligence. This is done through the image processing techniques. It uses the comparison of healthy plant leaves to that of the infected leaves and fruits. The classification of disease in the fruits and leaves are done accurately through the convolution neural network. This helps to detect the type and cause of the bacterial and fungal infections. This includes image pre-processing, data acquisition and image classification with feature extraction. Thus the CNN model helps to detect and classify the disease in plant and fruits in agricultural farm.

Classification of Automatic Detection of Plant Disease in Leaves and Fruits using Image Processing with Convolutional Neural Network

Identifying strong handwritten characters is a difficult task in the field of medical field and it is tedious process on decoding handwritten medicines. Recognition of handwritten mathematical expressions is a complex issue. The distribution and classification of specific characters makes the task more difficult. In our project, handwritten numbers and symbols are read and further addition, subtraction and multiplication operations are performed. The project includes a study about model deployment using convoluted neural networks and flasks. We use the CNN to classify specific characters. Tracking of character string operations are used to solve equations. The maximum accuracy of the proposed model is 99.12% recall is 95%, sensitivity is 89% and specificity is 68%. Effectiveness of our proposed system is helpful for students who want to get handwritten answers. The equation can be extended to more complex equations and more user data can be trained to improve correction and accuracy.

Performance Investigation of Handwritten Equation Solver using CNN for Betterment

Plant diseases pose a significant risk to global food supply and finances. This research paper develops a system using EfficientNet-B3 to classify plant leaf diseases. The PlantVillage dataset, with 54,303 curated images, is utilized. The model accurately identifies plant leaf diseases, with extensive testing and comparisons. A user-friendly web platform allows convenient submission and classification of plant leaf photos, providing prompt and reliable results for disease identification. The research has implications for revolutionizing plant disease detection and control strategies. By utilizing deep learning and EfficientNet-B3, this system offers an effective method for early disease detection, enhancing agricultural practices, and improving global food security.

Enhancing Plant Leaf Disease Classification Performance Through EfficientNetB3 and Hyperparameter Optimization

Agriculture is one of the most crucial determinants of a country's development. Agriculture employs more than 60% of the population in India. Plant disease is a persistent problem for smallholder farmers, posing a threat to income and food security as the seasons change. These diseases attack the leaves of the plant initially, then infect the entire plant, reducing the quality and quantity of the crop produced. It is impossible for a human to recognize and diagnose each plant's status due to a large number of plants on the farm. Because these ailments are contagious, it's vital to identify each plant. The deep convolutional Neural Network model's accuracy results in disease identification revealed that it is promising and can have a major impact on disease identification efficiency.

Identification of Maize Plant Leaf Disease Detection using ConvNet Model

Light absorption in the water medium causes degradation and distortion of under-water images. This study uses the technique is image restoration. It restores the images captured and adjusts the color gradient level of the images of underwater. Underwater photographs are not decorated and do not hide naturally. Therefore, to avoid this, various image restoration methods are used to retrieve the original image. This results suggest that the proposed approach has significantly enhanced the visibility and reliability of images in underwater. Two parameters are considered for the image enhancement that is BckL (Background Light) and TrMap (Transmission Map). Color and Contrast adjustment can be developed for the enhancement of the underwater image. The proposed method is developed by enhancing the color and contrast of the image by using the annotation of image manually. A database with the annotated image (ABckL) compared with the histogram model of underwater image statistical analysis is performed. TrMap is estimated roughly for the R channel with the new dark underwater (NDUI) images via a statistical model. Light attention (LA) and underwater reverse saturation (URS) are applied to NDUI to modify the course of the R channel to improve the image enhancement. The difference between red Channel (R) and channel- G-B estimates the TrMap of G-B channel. The white balance method at the end stages improves the color and contrast of the image. Compared with other methods, the proposed method produced has achieved high accuracy, short computation time, color improvement.

Improving Visibility of images - Underwater by Modeling of Back Light and Optimization of the Transmission Map

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Identification of Maize Plant Leaf Disease Detection using ConvNet Model

Improving Visibility of images - Underwater by Modeling of Back Light and Optimization of the Transmission Map