Fruit diseases play a major role in global agriculture, leading to substantial crop losses and influencing food production and economic stability. In this age of Industry 4.0 the fruit sorting is an important part in the food processing wherein this work plays a vital role. In this study, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. Deep learning models offer promise for automating disease identification using fruit images, but encounter obstacles such as therequirement for extensive training data, computational complexity, and the risk of overfitting. This study introduces an innovative convolutional neural network (CNN) architecture aimed at addressing these challenges by incorporating a reduced number of layers, thus alleviating computational burdens while maintaining performance. Additionally, augmentation techniques such as shift, shear, scaling, zoom, and flipping are employed to diversify the training set without additional image acquisition. Our CNN model is specifically trained to identify common apple crop diseases like Scab, Rot, and Blotch. Rigorous experimental evaluation demonstrates the effectiveness ofour model, achieving a remarkable classification accuracy of 95.37%. Significantly, our model demonstrates reduced storage requirements and faster execution times compared to existing deep CNN architectures, enabling deployment on handheld devices and resource-limited environments. While other CNN models may offer similar accuracy levels, our approach emphasizes efficiency and resource optimization, rendering it practical for real-world applications in agriculture. Furthermore, our CNN model exhibits resilience to environmental variations and imaging parameters, enhancing its applicability across diverse agricultural settings. By leveraging advanced machine learning techniques, the approach developed in this experimental work contributes to modernizing fruits and vegetables sorting operations in food processing, crop management practices thus promoting agricultural sustainability. The scalability and portability of our model make it suitable for deployment in both small-scale farms and large-scale agricultural operations. 

Using Machine Learning to Identify Diseases and Perform Sorting in Apple Fruit

Detection of diseases in plants is a significant task that has to be done in agriculture. This is something on which the economy profoundly depends. Infection discovery in plants is a significant job in the agribusiness field, as having diseases in plants is very common. To recognize the diseases in leaves, a continuous observation of the plants is required. This observation or continuous monitoring of the plants takes a lot of human effort and it is time-consuming too. To make it simply some sort of programmed strategy is required to observe the plants. Program based identification of diseases in plants makes easier to detect the damaged leaves and reduces human efforts and time-saving. The proposed algorithm distinguishing sickness in plants and classify them more accurately as compared to existing techniques.

Leaf Disease Detection and Classification based on Machine Learning

Crop plant diseases are a significant threat to productivity and sustainable development in agriculture. Early prediction of disease attacks is useful for the effective control of the disease by taking proactive actions against their attacks. Modern Information and Communication Technologies (ICTs) have a predominant role in Precision Agriculture (PA) applications to support sustainable developments. There is an immense need for solutions for the early prediction of the disease attack for proactive control against the plant disease attack. The present solution of disease detection using the computer vision approach can only detect the existence of the disease once the disease has already appeared. This study aims to propose a Machine Learning (ML) approach for the early prediction of the probability of disease attack based on Internet of Things (IoT) directly sensed crop field environmental conditions. Plant disease life cycles are strongly correlated with environmental conditions. The crop field environmental conditions are used to predict the occurrence of plant diseases. The Multiple Linear Regression (MLR) is applied as the ML model due to the existence of a linear relationship between disease attack and environmental conditions. Internet of Things (IoT) based crop field environmental conditions help to accurately predict the occurrence of plant diseases using the ML approach. The proposed model is implemented for the prediction of blister blight ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Exobasidium vexans ) for tea ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Camellia sinensis ) plant to check the effectiveness of the proposed solution. The implementation of the proposed model from 2015 to 2019 reveals that the accuracy of prediction of occurrence of the disease reached up to 91% in 2019.

Internet of Things (IoT) and Machine Learning Model of Plant Disease Prediction–Blister Blight for Tea Plant

Abstract One of the essential components of human civilization is agriculture. It helps the economy in addition to supplying food. Plant leaves or crops are vulnerable to different diseases during agricultural cultivation. The diseases halt the growth of their respective species. Early and precise detection and classification of the diseases may reduce the chance of additional damage to the plants. The detection and classification of these diseases have become serious problems. Farmers’ typical way of predicting and classifying plant leaf diseases can be boring and erroneous. Problems may arise when attempting to predict the types of diseases manually. The inability to detect and classify plant diseases quickly may result in the destruction of crop plants, resulting in a significant decrease in products. Farmers that use computerized image processing methods in their fields can reduce losses and increase productivity. Numerous techniques have been adopted and applied in the detection and classification of plant diseases based on images of infected leaves or crops. Researchers have made significant progress in the detection and classification of diseases in the past by exploring various techniques. However, improvements are required as a result of reviews, new advancements, and discussions. The use of technology can significantly increase crop production all around the world. Previous research has determined the robustness of deep learning (DL) and machine learning (ML) techniques such as k-means clustering (KMC), naive Bayes (NB), feed-forward neural network (FFNN), support vector machine (SVM), k-nearest neighbor (KNN) classifier, fuzzy logic (FL), genetic algorithm (GA), artificial neural network (ANN), convolutional neural network (CNN), and so on. Here, from the DL and ML techniques that have been included in this particular study, CNNs are often the favored choice for image detection and classification due to their inherent capacity to autonomously acquire pertinent image features and grasp spatial hierarchies. Nevertheless, the selection between conventional ML and DL hinges upon the particular problem, the accessibility of data, and the computational capabilities accessible. Accordingly, in numerous advanced image detection and classification tasks, DL, mainly through CNNs, is preferred when ample data and computational resources are available and show good detection and classification effects on their datasets, but not on other datasets. Finally, in this paper, the author aims to keep future researchers up-to-date with the performances, evaluation metrics, and results of previously used techniques to detect and classify different forms of plant leaf or crop diseases using various image-processing techniques in the artificial intelligence (AI) field. 

pdf/plant-disease-detection-and-classification-techniques-a-389a8gztid.pdf

Plant disease detection and classification techniques: a comparative study of the performances

Agriculture plays an essential role in Mexico’s economy. The agricultural sector has a 2.5% share of Mexico’s gross domestic product. Specifically, tomatoes have become the country’s most exported agricultural product. That is why there is an increasing need to improve crop yields. One of the elements that can considerably affect crop productivity is diseases caused by agents such as bacteria, fungi, and viruses. However, the process of disease identification can be costly and, in many cases, time-consuming. Deep learning techniques have begun to be applied in the process of plant disease identification with promising results. In this paper, we propose a model based on convolutional neural networks to identify and classify tomato leaf diseases using a public dataset and complementing it with other photographs taken in the fields of the country. To avoid overfitting, generative adversarial networks were used to generate samples with the same characteristics as the training data. The results show that the proposed model achieves a high performance in the process of detection and classification of diseases in tomato leaves: the accuracy achieved is greater than 99% in both the training dataset and the test dataset.

/pdf/monitoring-tomato-leaf-disease-through-convolutional-neural-1usvqpju.pdf

Monitoring Tomato Leaf Disease through Convolutional Neural Networks

Agriculture has become far more than simply a method to feed ever growing populations. It’s important wherever in additional than seventieth population of an Asian country is depends on agriculture. Which means it feeds nice range of individuals. The foremost necessary consider less amount crop of quality because of disease. Detecting disease may be a key to stop agricultural losses. The aim of this project is to develop a software system answer that Mechanically find and classify disease. The step like loading an image, pre-Processing, Segmentation, extraction and classification are involves illness detection. The leaves pictures are used for detecting the plant diseases. Therefore use of image process technique to find and classify diseases in agricultural applications is useful.

Identification of Plant Disease using Image Processing Technique

Recommender systems have become a vital tool to identify items for users based on personalized preferences. The personalized ranking or item recommendation generates a ranked list of items for the users. Clustering methods offer better scalability than collaborative filtering (CF) methods since they make predictions within small clusters. The major challenges of recommender systems are accuracy and scalability. Traditionally, recommendation systems are based on a centralized framework that restrains quick scalability for enormous data volumes. The emergence of cloud technology resolves this issue as it handles vast data and supports massive processing. This paper proposes a time cluster personalized ranking recommender system (TCPRRS) in a multi-cloud environment. TCPRRS is a five-stage system that generates recommendations based on temporal information of user consumption and clustering with personalized ranking. Particle swarm optimization (PSO) is utilized for optimizing the solution. The efficiency of TCPRRS is estimated using similarity metrics.

/pdf/time-cluster-personalized-ranking-recommender-system-in-2iwoc1fb.pdf

Time Cluster Personalized Ranking Recommender System in Multi-Cloud

Multi Objective Portfolio Optimization Models and Its Solution Using Genetic Algorithms

An approach to validate whether two cell phones are in closeness is vital in cell phone security For instance, the validation result can be utilized to combine two gadgets and develop a safe correspondence channel between them Many existing nearness confirmation frameworks depend on short range systems — the correspondence is typically confined in short range systems Be that as it may, this approach is insufficient when we need to check the correspondence separation is inside a couple of centimeters, ie close field To address this test, numerous different strategies build frameworks in light of the close field correspondence (NFC) framework Sadly, just a little part of keen gadgets in the present market are furnished with NFC chips The reason for this paper is to give a closeness confirmation framework which does not rely on upon NFC chips We devise a framework to accomplish nearness validation by utilizing related finger developments on the two cell phones Human info generally contains blunders and is of low entropy, which influences the ease of use and security of our framework We comprehend these issues in a productive way, considering the restricted computational assets on shrewd gadgets Our framework does not require any earlier mystery data shared between the two gadgets, and produces a similar high-entropy cryptographic key for both gadgets in a fruitful verification The productivity of the framework is approved by assessments on Motorola Droid cell phones

A presence verifying the identity system for smart phones

/pdf/data-security-concerns-in-approaches-to-overcome-cold-start-2iy959xt.pdf

Data Security Concerns in Approaches to Overcome Cold Start Problem in Recommender Systems - A Survey

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