Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Sharma, Abhinav; Jain, Arpit; Gupta, Prateek; Chowdary, Vinay

doi:10.1109/access.2020.3048415

Cited by 559 publications

(199 citation statements)

References 159 publications

(114 reference statements)

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“…Therefore, authors in the literature [7,8] emphasize the significance of enhancing farm management by using scientific strategies ply of food is becoming more significant and alarming as time progresses. Therefore, au thors in the literature [7,8] emphasize the significance of enhancing farm management b using scientific strategies and technology in the agricultural field, which, in turn, drast cally boost crop productivity and help in saving natural resources [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs

et al. 2021

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When integrating the Internet of Things (IoT) with Unmanned Aerial Vehicles (UAVs) occurred, tens of applications including smart agriculture have emerged to offer innovative solutions to modernize the farming sector. This paper aims to present a low-cost platform for comprehensive environmental parameter monitoring using flying IoT. This platform is deployed and tested in a real scenario on a farm in Medenine, Tunisia, in the period of March 2020 to March 2021. The experimental work fulfills the requirements of automated and real-time monitoring of the environmental parameters using both under- and aboveground sensors. These IoT sensors are on a farm collecting vast amounts of environmental data, where it is sent to ground gateways every 1 h, after which the obtained data is collected and transmitted by a drone to the cloud for storage and analysis every 12 h. This low-cost platform can help farmers, governmental, or manufacturers to predict environmental data over the geographically large farm field, which leads to enhancement in crop productivity and farm management in a cost-effective, and timely manner. Obtained experimental results infer that automated and human-made sets of actions can be applied and/or suggested, due to the innovative integration between IoT sensors with the drone. These smart actions help in precision agriculture, which, in turn, intensely boost crop productivity, saving natural resources.

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Section: Introductionmentioning

confidence: 99%

A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs

et al. 2021

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“…The biological method plays a significant and indispensable role in managing key pests of mulberry because application of chemical inputs like inorganic fertilizers, weedicides, insecticides, fungicides etc., in bimonthly interval in mulberry gardens not only pollute the ecosystem but also cause adverse impact on the soil health and hazardous effect on human beings and beneficial organisms including silkworms. Sharma et al [7] have proposed Machine Learning Applications for Precision Agriculture also known as smart farming has developed into an innovative tool for solving current agricultural sustainability challenges. The driving force behind this cutting-edge technology is machine learning (ML).…”

Section: Literature Surveymentioning

confidence: 99%

Mulberry Leaf Yield Prediction Using Machine Learning Techniques

Srikantaiah¹,

Deeksha²

2021

Atlantis Highlights in Computer Sciences

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Soil nutrients are essential for the growth of healthy crops. India produces a humungous quantity of Mulberry leaves which in turn produces the raw silk. Since the climatic conditions in India is favourable, Mulberry is grown throughout the year. Majority of the farmers hardly pay attention to the nature of soil and abiotic factors due to which leaves become malnutritious and thus when they are consumed by the silkworm, desired quality end-product, raw silk, will not be produced. It is beneficial for the farmers to know the amount of yield that their land can produce so that they can plan in advance. In this paper, different Machine Learning techniques are used in predicting the yield of the Mulberry crops based on the soil parameters. Three advanced machine-learning models are selected and compared, namely, Multiple linear regression, Ridge regression and Random Forest Regression (RF). The experimental results show that Random Forest Regression outperforms other algorithms.

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“…Loss caused to farmers by grass grub insect was studied in (8) . The extent of crop damage was assessed by known classifiers like DTs (Decision Tree), RF (Random Forest), NB (Naïve Bayes), SVM (Support Vector Machines) and KNN (K-Nearest Neighbors) (9) . Pouteau et al (10) compared 6 machine learning algorithms (SVM, Naïve Bayes, C4.5, RF, Boosted Regression Tree, and kNN) with 6 satellite data sets from different sensors (Landsat-7 ETM+, SPOT, AirSAR, TerraSAR-X, Quickbird, and WorldView-2) for Topical Ecosystems Classification and stated that kNN better performs for the Landsat-7 ETM+ classification.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning with Particle Swarm Based Hyper Parameter Tuning Based Crop Recommendation for Better Crop Yield for Precision Agriculture

Mythili¹,

Rangaraj²

2021

IJST

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The common difficulty present among the Indian farmers is that they don't opt for the proper crop based on their soil necessities. Because of this productivity is affected. This problem of the farmers has been solved through precision agriculture. This method is characterized by a crop database collected from the farm, crop provided by agricultural experts and given to recommendation system it will use the collect data and do deep learning model as learners to recommend a crop for site specific parameter with high accuracy and efficiency. Objectives: The comprehensive objective of the model is to deliver direct advisory services to even the smallest farmer at the level of his/her smallest plot of crop, using the most accessible technologies using deep learning. It is a recommender model built using a classifier and an optimization of the classifier. Based on appropriate parameters, the system recommends crops as technology based crop recommendation system in agricultural decisions can be of great help to farmers in increasing their crop yields or cultivating suitable crops based on their land characteristics and climatic parameters. Methods: Existing MLTs (Machine Learning Techniques) have been found to be expensive and fail to scale in locally collected samples, so recent techniques of deep learning method is adopted in this work. With this motivation, historical data on crop productions and climate data is used in this work which is then pre-processed followed by predictions using a Modified DNN (Deep Neural Networks) and PSO (Particle Swarm Optimization) called PSO-MDNN for recommending crop cultivation. This work uses L2 regularizations for reducing weights in matrices assuming. Findings: This work proposed MDNN where the weight matrices are calculated with L2 regularization and PSO utilized to tune the hyper parameters of MDNN and its network structure to improve https://www.indjst.org/

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Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Cited by 559 publications

References 159 publications

A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs

A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs

Mulberry Leaf Yield Prediction Using Machine Learning Techniques

Deep Learning with Particle Swarm Based Hyper Parameter Tuning Based Crop Recommendation for Better Crop Yield for Precision Agriculture

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