Comprehensive Review on Automated Fruit Disease Detection at Early Stage

Fruits are now cultivated in many different countries, which has increased global fruit output to 2,914.27 thousand tons. Numerous countries want to increase their fruit production in the next years, thus the number of countries producing fruits is expected to keep growing. But despite this, a variety of challenges and problems are still experienced while growing crops. These include problems with the fruit's general quality, the cost of manufacturing, the state of the seed, and the fruit's own illness. The main causes of fruit diseases' detrimental impacts are microbes and fungus. Early fruit disease detection is used to foresee fruit disease, which helps farmers save money by lowering the amount of capital they have to spend. To stop fruit illnesses in their early stages, it is crucial to figure out the best way to identify fruit infections. Many studies on a variety of fruits, including the papaya, apple, mango, olive, kiwifruit, orange, etc., have employed deep learning approaches. This study compares several ways for image capture, pre-processing, and segmentation as well as deep learning techniques. The study discovered that the best deep learning strategy for a particular collection of data may change depending on the system's computational power and the data being used. The results of this study show that a convolution neural network is more accurate and can predict a wide range of fruit diseases

Precision Agriculture using Internet of thing with Artificial intelligence: A Systematic Literature Review

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this revie

Guava Fruit Detection and Classification Using Mask Region-Based Convolutional Neural Network

Guava has various types and each type has different nutritional content, shapes, and colors. It is often difficult for some people to recognize guava correctly with so many varieties of guava on the market. In industry, the classification and segmentation of guava fruit is the first important step in measuring the guava fruit quality. The quality inspection of guava fruit is usually still done manually by observing the size, shape, and color which is prone to mistakes due to human error. Therefore, a method was proposed to detect and classify guava fruit automatically using computer vision technology. This research implements a Mask Region-Based Convolutional Neural Network (Mask R-CNN) which is an extension of Faster R-CNN by adding a branch that is used to predict the segmentation mask in each region of interest in parallel with classification and bounding box regression. The system classifies guava fruit into each category, determines the position of each fruit, and marks the region of each fruit. These outputs can be used for further analysis such as quality inspection. The performance evaluation of guava detection and classification using the Mask R-CNN method achieves an mAR score of 88%, an mAP score of 90%, and an F1-Score of 89%. It can be concluded that the proposed method performs well in detecting and classifying guava fruit

Effectiveness of Deep Feature Extraction Algorithm in Determining the Maturity of Fruits: A Review

Intelligent farming technology helps farmers overcome tough obstacles in the farming process, such as increased sup-plier costs, a lack of labour, customer satisfaction, and more. Artificial Intelligence (AI) is a remarkable technology in smart farming because it deeply understands the issue and can help farmers make decisions. This article's main objective is to identify and examine the concepts and techniques of Convolutional Neural Networks (CNN) technology that could aid in classifying the ripeness stages of fruit in intelligent farming. This paper systematically reviews 18 previous works for classifying the ripeness stages of fruit. This review outlines the most commonly used algorithms, activation functions, optimisation functions, and platforms for algorithm implementation. In addition, found that not all algorithms are suitable for even near-equivalent processes. Therefore, this study suggests the intensity of the CNN algorithms concerning various metrics to find the suitability for the operations/applications. Finally, this paper offers some future research directions in the ripeness classification of fruits

Biologically inspired robotic perception-action for soft fruit harvesting in vertical growing environments

Multiple interlinked factors like demographics, migration patterns, and economics are presently leading to the critical shortage of labour available for low-skilled, physically demanding tasks like soft fruit harvesting. This paper presents a biomimetic robotic solution covering the full ‘Perception-Action’ loop targeting harvesting of strawberries in a state-of-the-art vertical growing environment. The novelty emerges from both dealing with crop/environment variance as well as configuring the robot action system to deal with a range of runtime task constraints. Unlike the commonly used deep neural networks, the proposed perception system uses conditional Generative Adversarial Networks to identify the ripe fruit using synthetic data. The network can effectively train the synthetic data using the image-to-image translation concept, thereby avoiding the tedious work of collecting and labelling the real dataset. Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, our platform’s action system can coordinate the arm to reach/cut the stem using the Passive Motion Paradigm framework inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented. While this article focuses on strawberry harvesting, ongoing research towards adaptation of the architecture to other crops such as tomatoes and sweet peppers is briefly described

A brief review of artificial intelligence robotic in food industry

Artificial Intelligent Robotic (AI Robotic) is the formal term for an ‘intelligent induced’ robot that can operate with vision (eye) to detect a product defect, or a control operating system to assist robot to differentiate good and reject product during production. A number of cutting-edge manufacturing automation technologies have been developed, including automatic inspection, autonomous robots, additive manufacturing, ubiquitous manufacturing, cloud manufacturing, and cyber-physical systems. Intelligent and automation technologies for worker assistance have been considered feasible. As knowledge-driven industries develop in terms of artificial intelligence and robotic automation, and to upgrade the existing and next generation of workforce, higher and further education is needed to advance a better understanding of robotic automation in manufacturers who face endless challenges such as labor shortages, high turnover labor force, product innovation, quality matters, and many others. This paper provides a general view of food supply chain challenges when faced with labor issue, environmental disaster and production problems. AI Robotic application would be a good source of problem solving in food manufacturing, food processing and food handling proces

A brief review of artificial intelligence robotic in food industry

Artificial Intelligent Robotic (AI Robotic) is the formal term for an ‘intelligent induced’ robot that can operate with vision (eye) to detect a product defect, or a control operating system to assist robot to differentiate good and reject product during production. A number of cutting-edge manufacturing automation technologies have been developed, including automatic inspection, autonomous robots, additive manufacturing, ubiquitous manufacturing, cloud manufacturing, and cyber-physical systems. Intelligent and automation technologies for worker assistance have been considered feasible. As knowledge-driven industries develop in terms of artificial intelligence and robotic automation, and to upgrade the existing and next generation of workforce, higher and further education is needed to advance a better understanding of robotic automation in manufacturers who face endless challenges such as labor shortages, high turnover labor force, product innovation, quality matters, and many others. This paper provides a general view of food supply chain challenges when faced with labor issue, environmental disaster and production problems. AI Robotic application would be a good source of problem solving in food manufacturing, food processing and food handling proces